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Automotive Sheet Steel Stamping Process Variation An analysis of stamping process capability and implications for design, die tryout and process control. Auto/Steel Partnership
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Page 1: Automotive Sheet Steel Stamping Process Variation

AK Steel Corporation

Bethlehem Steel Corporation

DaimlerChrysler Corporation

Dofasco Inc.

Ford Motor Company

General Motors Corporation

Ispat/Inland Inc.

LTV Steel Company

National Steel Corporation

Rouge Steel Company

Stelco Inc.

U.S. Steel Group, a Unit of USX Corporation

WCI Steel, Inc.

Weirton Steel Corporation

Auto/SteelPartnership

This publication was prepared by:

Body Systems Analysis Project TeamThe Auto/Steel Partnership Program

2000 Town Center, Suite 320Southfield, Michigan 48075-1123248.356.8511 faxhttp://www.a-sp.org

A/SP-9030-3 0100 2M PROGPrinted in U.S.A.

Automotive Sheet Steel

Stamping Process Variation

An analysis of stamping

process capability and

implications for design,

die tryout and process

control.

Auto/Steel Partnership

Page 2: Automotive Sheet Steel Stamping Process Variation

Automotive Sheet SteelStamping Process Variation:

An Analysis of Stamping Process Capability and

Implications for Design, Die Tryout and Process Control

Auto/Steel Partnership ProgramBody Systems Analysis Project Team

2000 Town Center - Suite 320Southfield, MI 48075-1123

2000

Page 3: Automotive Sheet Steel Stamping Process Variation

Auto/Steel Partnership

AK Steel CorporationBethlehem Steel CorporationDaimlerChrysler Corporation

Dofasco Inc.Ford Motor Company

General Motors CorporationIspat Inland Inc.

LTV Steel CompanyNational Steel Corporation

Rouge Steel CompanyStelco Inc.

U. S. Steel Group, a Unit of USX CorporationWCI Steel, Inc.

Weirton Steel Corporation

This publication is for general information only. The material contained herein should not be used without first securing competent advice with respect to its suitability for any given application. This

publication is not intended as a representation or warranty on the part of The Auto/Steel Partnership – orany other person named herein – that the information is suitable for any general or particular use, or free from infringement of any patent or patents. Anyone making use of the information assumes

all liability arising from such use.

This publication is intended for use by Auto/Steel Partnership members only. For more information oradditional copies of this publication, please contact the Auto/Steel Partnership, 2000 Town Center, Suite

320, Southfield, MI 48075-1123 or phone: 248-945-7777, fax: 248-356-8511, web site: www.a-sp.org

Copyright 2000 Auto/Steel Partnership. All Rights Reserved.

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Table of Contents

Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi

Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.0 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.1 Motivation for Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2 Study Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2.0 Stamping Variation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.1 Components of Variation Explained . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.2 Calculating Components of Variation Using ANOVA . . . . . . . . . . . . . . . . . . . . . 92.3 Description of the Sources of Stamping Variation . . . . . . . . . . . . . . . . . . . . . . . 13

3.0 Analysis of Stamping Variation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143.1 Mean Conformance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3.1.1 Benchmark Comparison - Body Side Outer and Inner Panels . . . . . 143.1.2 Mean Bias and Part Tolerances . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173.1.3 Benchmark Comparison - Tryout versus Production . . . . . . . . . . . . . 183.1.4 Mean Bias Stability over Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183.1.5 Impact of Shipping on Mean Bias . . . . . . . . . . . . . . . . . . . . . . . . . . 21

3.2 Stamping Process Variation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213.2.1 Benchmark Comparison - Part-to-Part Variation . . . . . . . . . . . . . . . . 213.2.2 Variation Over Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243.2.3 Impact of Shipping on Variation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253.2.4 Components of Variation: Part-to-Part, Run-to-Run,

and Begin-End of Run . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253.2.5 Steel Properties and Press Setup Control and Stamping Variation . . 273.2.6 Effect of Mean Shifts on Statistical Process Control Techniques . . . . 29

4.0 Tolerance Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 344.1 Tolerances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 344.2 Cp and Cpk (Pp and Ppk) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 344.3 Recommended Tolerances for Sheet Metal . . . . . . . . . . . . . . . . . . . . . . . . . . . . 354.4 Part Tolerances and Functional Build . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

5.0 Conclusions and Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Appendix A - Part Sketches by Company . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

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iv

List of Figures

Figure 1. Body Side Components Chosen for Company C . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Figure 2. Components of Variation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Figure 3. Potential Sources of Stamping Variation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Figure 4. Total Variation Partitioned into Components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Figure 5. Body Side Outer for Company A: 12 Measurement Locations . . . . . . . . . . . . . . . . . 12

Figure 6. Histogram of Mean Values across 5 Parts for Company C . . . . . . . . . . . . . . . . . . . . 15

Figure 7. Mean Conformance: Rigid vs. Non-Rigid Panels . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Figure 8. Mean Conformance: Two-Piece Body Side Panel vs. One-Piece . . . . . . . . . . . . . . . 16

Figure 9. Correlation of Mean at Part Approval vs. Production . . . . . . . . . . . . . . . . . . . . . . . . . 19

Figure 10. Effect of Stamping Mean Shift on Body Side Assembly . . . . . . . . . . . . . . . . . . . . . . 20

Figure 11. Average Variation (Standard Deviation) by Type of Part . . . . . . . . . . . . . . . . . . . . . . 23

Figure 12. Part-to-Part Variation: Home Line Tryout Approval vs. Production, by Dimension . . . 24

Figure 13. Components of Variation for Body Side Panel at Company C and Company D . . . . . 26

Figure 14. Relationship between Press Tonnage and Mean Shift Variation ( mean shift) . . . . . 29

Figure 15. X-Bar/Range Chart vs. Individuals/ Moving Range Charts . . . . . . . . . . . . . . . . . . . . 32

Figure 16. Illustration of Cp and Cpk calculations for three scenarios . . . . . . . . . . . . . . . . . . . . 35

Figure 17. Part Sketches at Company A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

Figure 18. Part Sketches at Company B . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

Figure 19. Part Sketches at Company C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Figure 20. Part Sketches at Company D . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Figure 21. Part Sketches at Company E . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

Figure 22. Part Sketches at Company F . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

Figure 23. Part Sketches at Company G . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

Page 6: Automotive Sheet Steel Stamping Process Variation

v

List of Tables

Table 1. Participating Automotive Manufacturers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Table 2. Components Studied at Each Automotive Manufacturer . . . . . . . . . . . . . . . . . . . . . . 5

Table 3. Formulae for Calculating Components of Variation . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Table 4. 36-Data Samples for a Stamping Dimension . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Table 5. SPSS Output Calculations for Mean Squared Errors (all factors) . . . . . . . . . . . . . . . . 11

Table 6. SPSS Output Calculations for Mean Squared Errors without Begin-End Factor . . . . . 11

Table 7. Summary of Components of Variation Calculations . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Table 8. Variance Summary for twelve Body Side Dimensions . . . . . . . . . . . . . . . . . . . . . . . . 12

Table 9. Mean Conformance by Company . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Table 10. Mean Bias by Type of Part . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Table 11. Mean Conformance and Tolerances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Table 12. Summary of Mean bias: Tryout vs. Production - Case Study Parts . . . . . . . . . . . . . . 18

Table 13. Comparisons of the Change in Mean Bias from Tryout to Home Line . . . . . . . . . . . . 19

Table 14. Change in Mean from Home Line to Long-term Production . . . . . . . . . . . . . . . . . . . 20

Table 15. Summary of Panels Measured Before and After Shipping . . . . . . . . . . . . . . . . . . . . . 21

Table 16. Part-to-Part Variation for the Body Side Outer Panels . . . . . . . . . . . . . . . . . . . . . . . . 22

Table 17. Effect of Dimension Location on Variation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Table 18. Part-to-Part Variation: Home Line Approval vs. Production, by Company . . . . . . . . . 24

Table 19. Summary of Remeasured Data Before and After Shipping via truck . . . . . . . . . . . . . 25

Table 20. Summary of Part-to-Part and Total Variation for the Body Side Outers . . . . . . . . . . . 25

Table 21. Sources of Variation by Part for Company A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Table 22. Sources of Variation by Part for Company C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Table 23. Summary of Product and Process Variation Compliance . . . . . . . . . . . . . . . . . . . . . 28

Table 24. Summary of Mean Shift Variation across Companies . . . . . . . . . . . . . . . . . . . . . . . . 30

Table 25. Process Control Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Table 26. Effect of Stamping Mean Shifts on Assembly Variation . . . . . . . . . . . . . . . . . . . . . . . 33

Table 27. General Recommended Tolerances for Stamped Parts Based upon Process Capability . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

Page 7: Automotive Sheet Steel Stamping Process Variation

Preface

This report is one of a series published by theAuto/Steel Partnership Body Systems AnalysisProject Team on stamping and assembly variation,body measurement systems and process valida-tion. These reports provide a summary of the proj-ect research and are not intended to be all inclu-sive of the research effort. Numerous seminarsand workshops have been given to individualautomotive manufacturers throughout the projectto aid in implementation and provide direct techni-cal support. Proprietary observations and imple-mentation details are omitted from the reports.

This automotive body development report,"Stamping Process Variation: An Analysis ofStamping Process Capability and Implications forDesign, Die Tryout and Process Control," updatesongoing research activities by the Body SystemsAnalysis Team and the Manufacturing Systemsstaff at The University of Michigan's Office for theStudy of Automotive Transportation.

An over-riding goal of this research is to developnew paradigms that will drive automotive body-in-white development and production towards a totaloptimized processing system. Previous reportsdescribed fundamental research investigatingsimultaneous development systems for designing,tooling and assembling bodies, and flexible bodyassembly. Since the inception of this research pro-gram, considerable emphasis has been focusedon benchmarking key world class body develop-ment and production processes. These bench-marks created foundation elements upon whichfurther advances could be researched and devel-oped.

This report summarizes recommendations formoving toward a new "functional build" paradigmby tightly integrating the many individual activitiesranging from body design and engineeringthrough process and tooling engineering. Revisedstamping die tryout and buyoff processes receivespecial emphasis, as does the launch of stampingand assembly tools.

The researchers are indebted to several globalautomotive manufacturers for their on-going dedi-cation and participation in this research. Theyinclude DaimlerChrysler Corporation, Ford MotorCompany, General Motors Corporation, Nissan,NUMMI (Toyota), Opel and Renault. Each con-ducted experiments under production conditionsinvolving hundreds of hours of effort and oftenrequiring the commitment of many productionworkers and engineering personnel. Although itmay be impractical to mention each one of thesepeople individually, we do offer our sincere appre-ciation.

These reports represent a culmination of severalyears of effort by the Body Systems AnalysisProject Team. Team membership, which hasevolved over the course of this project, includes:

J. Aube, General Motors CorporationH. Bell, General Motors CorporationC. Butche, General Motors CorporationG. Crisp, DaimlerChrysler CorporationT. Diewald, Auto/Steel PartnershipK. Goff, Jr., Ford Motor CompanyT. Gonzales, National Steel CorporationR. Haan, General Motors CorporationS. Johnson, DaimlerChrysler CorporationF. Keith, Ford Motor CompanyT. Mancewicz, General Motors CorporationJ. Naysmith, Ronart IndustriesJ. Noel, Auto/Steel PartnershipP. Peterson, USXR. Pierson, General Motors CorporationR. Rekolt, DaimlerChrysler CorporationM. Rumel, Auto/Steel PartnershipM. Schmidt, Atlas Tool and Die

The University of Michigan TransportationResearch Institute conducted much of theresearch and wrote the final reports. The principalresearch team from the Manufacturing SystemsGroup was:

Patrick Hammett, Ph.D. (734-936-1121/[email protected]) Jay Baron, Ph.D. (734-764-4704/[email protected])Donald Smith, Associate Director (734-764-5262)

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Page 8: Automotive Sheet Steel Stamping Process Variation

Executive Summary

The Auto/Steel Partnership (A/SP) is an innovativeinternational association that includesDaimlerChrysler, Ford, General Motors and elevenNorth American sheet steel producers. ThePartnership was formed in 1987 to leverage theresources of the automotive and steel industries topursue research projects leading to excellence inthe application of sheet steels in the design andmanufacture of vehicles. The Partnership hasestablished project teams that examine issuesrelated to steel properties including strength, dentresistance, surface texture and coating weights,as well as manufacturing methods includingstamping, welding and design improvements.

Automotive manufacturers face the challenge ofidentifying when a process is capable of produc-ing dimensionally acceptable stamped panels.The non-rigid nature of many stamped parts hasalways made them difficult to measure. Oftenparts do not meet the dimensional quality objec-tives, as measured by Cpk, seen in many othervehicle components. In fact, no manufacturer hassuccessfully achieved a Cpk of 1.33 on all partdimensions using the original specifications. Thisis particularly true for the larger, lighter gaugebody panels. Furthermore, achieving a high Cpk

value alone is not necessarily a good predictor offinal dimensional quality. Factors, such as therigidity of the mating panels, the assembly locat-ing process and the clamp and welding effects,influence how body panels build into an assembly.Consequently, a number of automotive manufac-turers have opted not to use Cpk as the principalmeasure of panel quality.

This stamping report analyzes dimensional data tocharacterize stamping variation by short-term(part-to-part), long-term (die set to die set), andmean bias (long-term deviation from design nomi-nal) to better understand process capability.Numerous factors affect the observed variation ina stamping process, making stamping one of themore difficult processes to control. The complexityof stamping makes it extremely difficult to conductrigorous experimental studies that can be general-ized beyond a given part and process configura-tion. Thus, the knowledge base of stamping varia-tion is very sparse, and a great opportunity exists

to learn and to apply this knowledge to automotivebody evaluation processes including die buy-off,production validation and long-term processcapability analysis. Working within the constraintsof the production environment, this research eval-uated stamping variation for several processesacross the seven manufacturers. The researchfound that stamping variation is related to:

• Check point location on a part: More rigid areastend to be closer to nominal and have less variation.

• Measurement fixture design: Checking fixtureswith more clamps tend to reflect lower variation.

• Part size, complexity and thickness: Smaller, lesscomplex and thicker parts have less variation.

• Press process control: Different press linesdemonstrate higher die set to die set mean shiftcontrol which often is reflected in the control ofprocess variables such as draw press tonnage.

• Shipping and handling: The shipping and han-dling of parts tends to increase variation andshift dimensions on the parts.

• Changes in stamping presses: Some dimension-al shifts occur as dies are moved from a tryoutpress line to the home production press line.

Different automotive manufacturers manage varia-tion, in part, by how they manage these factors andseveral examples are cited in this report. Althoughthe effects of steel material properties such asgauge, yield strength, percent elongation and n-value were investigated, this factor is not includedin the list above because it had minimal influenceon variation. All of the manufacturers that suppliedsteel coupons in this study had material propertiessufficiently controlled to virtually eliminate any influ-ence on stamping variation.

One of the objectives of this research is to under-stand the amount of variation experienced by dif-ferent manufacturers and how they manage varia-tion issues. Together, this information may be usedto improve the overall validation process forstamping and sheet metal assembly. The uncer-tainty of sheet metal assembly clearly supports afunctional build approach where component qual-ity is determined by how it influences the assem-bly. These methods are outlined in other reports bythe Body Systems Analysis Project Team.

1

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1.0 Introduction

1.1 Motivation for Research

Leading global automotive manufacturers havebeen challenged with applying traditional designpractices to sheet metal design and assembly.The goal behind this effort is to help achieve highquality car bodies with minimal lead-time anddevelopment costs. These practices include geo-metric dimensioning and tolerancing (GD&T), vari-ation simulation analysis, tolerance stack-upanalysis and setting quality standard targets forprocess capability, such as Cp and Cpk. Most man-ufacturers have expressed concerns over the lim-ited success these methods have had on sheetmetal processes including the assignment ofdimensional part tolerances, translating compo-nent designs into tools that can make them andpredicting assembly conformance based onstamping capability. Several important observa-tions account for the limited success of applyingtraditional design principles to these processes:

• Manufacturers experience difficulties estimatingmean part dimensions, relative to nominal andprocess variation because these attributes areproduct and process co-dependent. Potentialattributes affecting variation include materialproperties (steel variations in gauge, grade, andcoatings), part geometry (size and shape), dieengineering and construction, and stampingpress variables. The infinite number of designand process possibilities make it nearly impossi-ble to accumulate sufficient historical knowledgefor a designer to accurately assign tolerancesthat consistently meet future process capability.

• The lack of component rigidity allows less stablepanels to conform to more rigid ones, making itdifficult to predict final assembly dimensionsbased on component quality.

• Component dimensions that deviate from theirdesign nominal cannot always be predictablycentered or shifted to the desired nominal with-out excessive rework costs. Moreover, thisrework may correct one particular deviation butadversely affect correlated points on the samepart.

• Part measurement systems often have limitedcapability to measure non-rigid parts without

additional clamps beyond 3-2-1 requirements.These additional clamps in the fixtures over-con-strain parts, thereby shifting mean dimensions.

• Stamping processes have so many input vari-ables affecting variation, with some estimates atwell over 100, that even world-class stampingoperations routinely operate outside of statisticalcontrol, with non-stable process means betweendie sets, especially on larger flimsy parts.Consequently, measuring several parts from asingle die set or run does not provide sufficientinformation about the expected long-term varia-tion of the process.

• Assembly processes often distort parts - some-times closer to and sometimes further away fromnominal - during assembly because of clamping,spot welding, and inconsistencies of part locat-ing schemes. These distortions can shift panelmean dimensions and affect process variation,resulting in a low correlation between stampingdimensions and assembly dimensions.

A purpose of this report is to provide a basicunderstanding of stamping variation. The data inthis report are intended to illustrate general char-acteristics of stamping variation, and are notintended to be a comprehensive data base to sup-port design. World-class automotive manufactur-ers that are most adept at designing, producingand assembling sheet metal are those who haveeffectively learned from past designs, while man-aging the variation in new parts and processes asthey become known. By researching a number ofstamping and assembly processes across severalmanufacturers, this report begins to establishboundaries for the limits of variation that can beexpected under different situations. This reportexamines the implications of this inherent stamp-ing variation on several design and validationactivities including:

• Tolerance assignment,• Check point selection,• Stamping process control limits,• Process validation - die tryout,• Production part approval process - stamping,• Part measurement systems and measurement

strategies, and• Assembly strategies with respect to part locating

and clamping.

2

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The majority of the data in this report was collect-ed under production conditions, resulting in sever-al advantages and disadvantages over a morecontrolled experiment approach. The advantagesare that the data actually reflect what can beexpected in production at normal line rates and with typical levels of process control.Generalizations about process variation are madewhere similar observations are seen over severalcase studies. The disadvantages are that the datacannot be used, in most instances, to supportdirect cause-and-effect conclusions, thus oftenlimiting observations to hypotheses. However,given the infinite number of part and process

design options, controlled experiments in stamp-ing and assembly often have a limited value ingeneralizing results.

1.2 Study Background

The seven automotive manufacturers noted partic-ipated in this study by providing data about theirstamping and assembly processes. These manu-facturers, and the respective vehicles studied areshown in Table 1 below. Note that companies are referred to as A, B, C, D, E, F, and G in thisreport and they do not correspond to the orderpresented.

3

Table 1. Participating Automotive Manufacturers

Data Stamping AssemblyCompany Model Collection Location Location

GM Grand Am 1996 Lansing, MI Lansing, MI

NUMMI (Toyota) Corolla 1996 Fremont, CA Fremont, CA

DaimlerChrysler Neon 1997 Twinsburg, OH; Belvidere, IL Belvidere, IL

Nissan Altima 1997 Smyrna, TN Smyrna, TN

Ford Taurus 1997 Chicago, IL Chicago, IL

Opel Vectra 1998 Ruesselsheim, Germany Ruesselsheim, Germany

Renault Clio II 1998 Flins, France Flins, France

Page 11: Automotive Sheet Steel Stamping Process Variation

Dimensional studies at each manufacturer arebased on the body side assembly and its majorstamped components. One participant provideddata for panels on both the right and left bodyside, resulting in a total of eight body-side assem-bly studies. The difference among manufacturerswas the type of body side outer. Three used bodyside outers with an integrated quarter panel whilethe remaining manufacturers used two-piece bodysides. The other panels chosen in each body sideassembly study, typically 4-5 mating parts,depended on the design, but with the goal toinclude critical rigid structural reinforcements with thicker gauges greater than 1.25 mm.

The scope of body panels included from all theautomotive manufacturers are:

• One-piece body side outer,• Two-piece body side outer,• Center pillar reinforcement (B-pillar),• Front pillar reinforcement (A-pillar),• Quarter outer panel,• Quarter inner panel,• Roof rail outer,• Wheelhouse outer, and• Windshield frame reinforcement.

Figure 1 below illustrates a typical body side casestudy for a two-piece body side.

4

Figure 1. Body Side Components Chosen for Company C

Windshield FrameReinforcement

Front PillarBody Side

Center Pillar

Roof Rail

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5

Table 2. Components Studied at Each Automotive Manufacturer

Company Part Number of Steel SteelIdentifier Description Dimensions Gauge Coupons

Body Side – One Piece 39 0.69

Quarter Inner 76 0.90

A Wheelhouse Outer 38 0.61 Yes

Front Pillar Reinf 69 1.70

Center Pillar Reinf 60 1.44

Body Side – One Piece 104 0.90

Body Side Inner 54 0.80

Wheelhouse Outer 42 0.75

B Center Pillar Reinf 17 1.00 Yes

Cowl Side 24 1.10

Roof Rail Inner 8 0.80

Roof Rail Outer 13 1.00

Body Side – Two Piece 60 1.10

Roof Rail Outer 22 0.90

C Front Pillar Upper 9 1.85 Yes

Front Pillar Lower 8 1.85

Center Pillar Reinf 14 1.87

Windshield Side Inner 30 2.70

Body Side – Two Piece 17 0.73

Quarter Otr 14 0.82

D Front Pillar Lower 6 Yes

Center Pillar Lower 6

Front Pillar Upper 15

Center Pillar Upper 4

Body Side – Two Piece 35

E Front Pillar Lower 2 No

Center Pillar Lower 2

Roof Rail 2

Body Side – One Piece 38 0.90

F Quarter Outer 11 No

Body Side Inner 6

Center Pillar 6

Body Side – Two Piece 54 Frt. 1.17;(tailor welded blank) Rr: 0.77

Body Side Inner 13 0.67

G Windshield Side Inner 5 1.17 Yes

Front Pillar Reinf 6 0.97

Center Pillar Reinf 10 1.17

Table 2 below lists the body side components cho-sen from each of the automotive manufacturers forthis study. Each of the seven manufacturers is

identified consistently by the same letter, Athrough G, throughout the report. (See Appendixfor sketches of components in study.)

Page 13: Automotive Sheet Steel Stamping Process Variation

A consistent sampling plan was applied to each ofthe stamped panels. This plan was designed toascertain both short-term and longer-term varia-tion under production conditions. Six panels weretaken during each die set or production run for agiven part: three consecutive panels near thebeginning of the run and three consecutive panelsnear the end of the run. This six panel samplingplan was then repeated over six separate die sets,thus producing a total case study of 36 panels (6per die set x 6 die sets = 36 panels). This sam-pling plan was executed on all of the major panelsin each case study. A few smaller reinforcementshad less than 6 die sets. The length of each die setvaried by participant, but tended to be greaterthan four hours. The time between each die setupalso varied and was typically between 2 and 7days. This sampling plan allowed the calculationsof short-term variation, or variation across consec-utive panels, and long-term variation, or variationboth within and between die sets.

Several of the manufacturers also collected panelcoupons and process variable data to see if rela-tionships could be found between the material orequipment setup and stamped panel variation.They collected a steel blank at the destacking sideof the press while a contiguous sample of 3 pan-els was collected. At the same time, several com-panies collected data on the process, such as ton-nage and cushion pressure in the draw die. Theactual variables collected at each manufacturervaried by part and die design. The steel couponswere collected and tested for several properties,including R-value, n-value and blank gauge varia-tion.

The measurement of the parts was conducted in amanner to reduce potential error. The 36 sampleswere collected over a period of several weeks andset aside for measurement at one time. Thisapproach was intended to reduce measurementerrors by using a single operator and a standardmeasurement protocol of a loading, clamping andmeasurement sequence. To measure the bodyside outer and many of the other inner panels, fivemanufacturers used CMMs, while E and F usedhard fixtures. A few of the smaller parts weremeasured on hard checking fixtures usingdatamyte collection devices and measurementprobes. In all cases, part locating was based onthe standard checking fixtures used by each man-ufacturer for internal quality monitoring. A fewchose to modify their CMM measurement routinesto include additional dimensions to provide a morecomprehensive geometric database.

One challenge with comparing manufacturers inthis study was the significant differences in meas-urement systems. These differences relate prima-rily to the locating and clamping of parts in the fix-tures. Some manufacturers attempt to minimizethe influence of the fixture on the part by minimiz-ing the number of clamps and clamping pressure,while others intentionally over-constrain their partsfor measurement. Manufacturers attempting toreduce the influence of the measurement systemuse a minimal number of clamps and locators toobtain an adequate measurement system repeata-bility and reproducibility, or gage R&R. Other man-ufacturers more readily obtain high gage repeata-bility and reproducibility by adding a larger num-ber of clamps. This approach, masks variation inthe panel, making the measurement system lessable to detect variation. The difference in meas-urement systems requires caution when generaliz-ing variation across manufacturers.

6

Page 14: Automotive Sheet Steel Stamping Process Variation

7

2.0 Stamping Variation

2.1 Components of Variation Explained

Dimensional variation from the stamping processmay be categorized into a number of components.Generally, different variation components areattributable to different sources and often have adifferent impact on downstream operations. Thefollowing are the general components of variationthat will be used throughout this report. Figure 2below illustrates each variation component.

• Mean bias deviation is the process bias relativeto the design nominal. Mean bias is the absolutevalue of the average deviation from nominal.When a process is centered exactly at its nomi-nal dimension, its mean bias is zero. If, after asingle die set, for example at the die source try-

out, a mean dimension is -0.65 mm or 0.65 mm,then its mean bias is 0.65 mm. If the mean fromtwo die sets are -0.40 mm and 0.10 mm, assum-ing equal sample size from each die set, then thegrand mean is -0.15 and the mean bias of thosetwo die sets is 0.15 mm ([-0.40 + 0.10] ÷ 2 =0.15).

• Part-to-part variation is also referred to as theshort-term or inherent variation. It is the amountof variation that can be expected across con-secutive parts produced by the process during agiven run. The assumption is that the variation isa reflection of numerous incidental random vari-ables over a short-term and is not affected byany special causes of variation such as achange in the steel coil or process settings. Thispart-to-part variation is denoted as part-part.

Figure 2. Components of Variation

Tryout Regular Production

Die Source to Home LineMean Shift

Mean Shift

Die Source TryoutMean Bias

UpperSpecification

LowerSpecification

Nominal

Legend:

Individual Measurements

Mean of the Stamping Run

Part-to-Part Total Variation

Mean Bias

1.5

1

0.5

0

-0.5

-1

Page 15: Automotive Sheet Steel Stamping Process Variation

8

Estimates for part-to-part variation for the 36-panel study are based upon the 12 sub-groups of 3 consecutive panels. Again, theassumption is that the process is stable duringthree consecutive parts.

• Run-to-run variation is commonly referred to asmean-shift variation. It is the measure of therepeatability of the die setting process, and itsderivation is based on the variation of the meandimension across two or more die sets. Run-to-run variation is denoted as run-to-run. Estimatesfor run-to-run variation are based on the varia-tions in mean dimensions between die sets.

• Begin-end of run variation is another type ofmean-shift variation in that it is a measure of thestability of the process mean within a run. Sincestamping production runs can be long, the meanof the run can change from the beginning to the

end, which may be several hours later. Thischange in a mean dimension may occur due toprocess changes during a run such as a steel coilchange, changes in operating speeds or tonnage,or adjustments to draw lubrication. If a meandimension significantly shifts during a run due tosome special cause, the stable mean assumptionis violated and begin-end variation is greater thanpart-to-part variation. We denote this variation as

begin-end. Estimates for begin-end of run variationare based on the variation of the mean dimensionfrom the beginning to the end of each die set.

Figure 3 below illustrates a run chart for a singlestamping dimension with unusually large variation.Each of the three variation components, part-to-part, run-to-run, and begin-end variation, is illus-trated in the plot.

Figure 3. Potential Sources of Stamping Variation

Run Chart of a Stamping Check Point

Part -to- part

Measurement Values Mean of Group

4

3

2

1

0

-1

-2

within runmean shift

run-to-runmean shift

run 1 run 2 run 3 run 4 run 5 run 6

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35

Mea

sure

men

t Val

ue

(mm

)

Page 16: Automotive Sheet Steel Stamping Process Variation

9

• Mean shift variation is the sum of the run-to-runvariation and begin-end of run variation. Sincerun-to-run variation and begin-end variation areboth forms of mean instability, they can be com-bined into one variation number that is called themean shift variation. The mean shift variation isdenoted by mean shift, where 2 mean shift = 2 run-

to-run + 2 begin-end. In most cases with stamping,the run-to-run variation dominates the within runvariation ( 2 run-to-run >> 2 begin-end), so ratherthan separate the two, the total mean shift varia-tion ( 2 mean shift) is used.

• Total variation is the sum of part-to-part variationand mean shift variation. This represents thetotal variation that the downstream assemblyprocess is subject to over the long-term. Thetotal variation is denoted total. Equation 1 andFigure 4 below summarize variation partitionedinto components.

Total variation is equal to the sum of the compo-nents of variation:

2 total = 2 part-to-part + 2 mean shift, or

Equation 12 total = 2 part-to-part + 2 run-to-run + 2 begin-end

2.2 Calculating Components of VariationUsing ANOVA

An efficient method for estimating the componentsof variation is through Analysis Of Variance, orANOVA. Briefly, the parameters for an ANOVAmodel for this sampling plan were defined in thefollowing manner:

d = number of die sets = 6 s = number of groups = 2 samples of 3

per die set per batchn = sample size per group = 3 consecutive

panels

The total number of panels sampled is equal todsn, or 6x2x3 = 36. This ANOVA model estimatespart-to-part, run-to-run and begin-to-end of runvariation using the expected Mean Squares (MS).The equations shown in Table 3 on page 10 areused to estimate the sources of variation. If both factors of run-run and begin-end of run arestatistically significant, then a begin-end of runand a run-run variance may be calculated. If onlyone of the two factors is significant, then only thatvariable will have a variance estimate. Finally, ifneither of the two factors is significant, then all ofthe total variance may be attributed to part-partvariation.

2

(inherent processvariation)

total

2part - to - part

2run - to - run

2begin-end

2mean shift

Figure 4. Total Variation Partitioned into Components

Page 17: Automotive Sheet Steel Stamping Process Variation

10

The following table illustrates an application of anANOVA analysis for a stamping dimension.

2Equation 2 = MSE mean squared error

(mean squared – meansquared error) – sample size

= (MSBE – MSE)n

Variation Formula Description

=

=

Equation 3

Equation 4a*

Equation 4b*

part-to-part

2begin-end run begin-end

2run-to-run

2run-to-run

(MSRR – MSBE)sn

(MSRR – MSE)sn

begin-end

run-to-run

run-to-run

(mean squared – meansquared error ) – (numberof samples x sample size)

(mean squared – meansquared error) – (number of samples x sample size)

Table 3. Formulas for Calculating Components of Variation

*Note: If all variation sources are significant, use Equation 4a. If begin-end factor is not significant, use Equation 4b.

Table 4. 36-Data Samples for a Stamping Dimension

Die Set Group Panel 1 Panel 2 Panel 3 Sample Average

1 begin run 0.13 0.16 0.17 0.15

1 end run 0.10 0.03 0.03 0.05

2 begin run 0.06 0.16 0.08 0.10

2 end run 0.13 0.08 0.23 0.15

3 begin run 0.18 0.72 0.14 0.34

3 end run 0.19 0.49 0.16 0.28

4 begin run -0.35 -0.43 -0.47 -0.42

4 end run -0.41 -0.39 -0.38 -0.40

5 begin run -0.32 -0.31 -0.35 -0.33

5 end run -0.32 -0.33 -0.29 -0.31

6 begin run -0.16 -0.10 -0.21 -0.16

6 end run -0.20 -0.17 -0.20 -0.19

Grand Mean (Mean Bias) -0.06 (.06)

Page 18: Automotive Sheet Steel Stamping Process Variation

11

Table 5. SPSS Output Calculations for Mean Squared Errors (all factors)

a. Mean square run-to-runb. Mean square begin-run of runc. Mean squared error

Source df Mean Square F Significance

Run-Run Hypothesis 5 .479a 103.81 .000

Error 6 4.6E-03b

Run-Run x Hypothesis 6 4.6E-03b .352 .901

Begin-End Run Error 24 0.013c

Table 6. SPSS Output Calculations for Mean Squared Errors without Begin-End Factor

Source df Mean Square F Significance

Run-Run Hypothesis 5 .479 25.836 .000

Error 30 0.011

The ANOVA output for this data is summarized inTable 5 below, or using the Statistical SoftwarePackage SPSS based on Type I error, = 0.05.Note that a significant variable has a value less

than . For this data set, the Mean Squared Errorfor the begin-end factor is not significant. That is,the mean does not significantly change from thebeginning to the end of the stamping run.

Since the begin-end factor is not significant, theANOVA model must be revised and the MeanSquare Errors recalculated. The revised SPSS out-

put is shown in Table 6, where again significanceis based on a Type I error of 0.05.

The mean squared errors in Table 6 above may beused to estimate the variation for each of the com-ponents of variation present using Equations 2

through 4 (note: begin-end = 0 because this factoris not significant for this dimension). The variationestimates are shown in Table 7 below.

Table 7. Summary of Components of Variation Calculations

Variation Source Mean Squared Error Calculation Variance (mm2) Standard Deviation (mm)

Part-to part MSE 0.011 0.11

Begin-end run Not significant 0.00 0.00

Run-to-run (0.479 – 0.011) ÷ (2)(3) 0.078 0.28

Total Process (.078+.011) 0.089 0.30

Page 19: Automotive Sheet Steel Stamping Process Variation

Table 8 below summarizes the components of vari-ation and the mean bias at 12 measurement loca-tions for the Company A Body Side Outer as,shown in Figure 5. One noteworthy finding is thelarge range in variation for part-to-part, 0.01 to0.48, and run-to-run, 0.00 to 0.18, across differentmeasurement locations. This contrast is attributa-ble to the differences in location/axis on the partand also to the proximity of measurement system

clamps. It will be shown in the following sectionthat as the number of clamps increases on achecking fixture, the amount of observed variationdecreases due to the masking of variation by theclamps. Table 8 also indicates that part-to-partvariation, 65.4%, and run-to-run variation, 30.3%,are much greater than begin-end run variation of4.3% for these dimensions.

12

Figure 5. Body Side Outer for Company A: 12 Measurement Locations

#10 & #11

#12

#8

#9

#7

#5

#4

#1

#2#3

#6

Table 8. Variance Summary for 12 Body Side Dimensions

Measurement Component of Variation (mm2)Part-to-Part Begin-end Run-to-run Total Process Mean Bias (mm)

Location Direction ( 2 part-to-part) ( 2 begin-end) ( 2 run-to-run) ( 2 total)

1 Y 0.09 0.00 0.06 0.16 0.313

2 Y 0.04 0.00 0.00 0.04 0.342

3 Z 0.01 0.01 0.00 0.02 0.786

4 Y 0.05 0.03 0.00 0.08 1.187

5 X 0.48 0.00 0.00 0.48 0.851

6 Z 0.05 0.05 0.00 0.10 3.673

7 Z 0.06 0.00 0.03 0.09 1.609

8 Y 0.14 0.00 0.18 0.32 2.530

9 X 0.34 0.00 0.17 0.51 1.139

10 Y 0.01 0.00 0.00 0.01 0.618

11 Z 0.02 0.00 0.03 0.05 0.837

12 Y 0.14 0.00 0.18 0.32 0.675

Average 0.12 0.10 0.05 0.18 1.130

Percent of Total 65.4% 4.3% 30.3% 100.0 ––

Page 20: Automotive Sheet Steel Stamping Process Variation

2.3 Description of the Sources of StampingVariation

Extensive research has been conducted regard-ing dentification and elimination of the sources ofvariation associated with stamping sheet metal.The stamping process is complex, with many variables that can influence variation. One relatedresearch effort, by John Siekirk,(1) identified 30major factors, and then classified them into the following seven categories:

• Blank condition,• Blank lubrication,• Stamping press variables,• Metal properties,• Die condition,• Miscellaneous and• Interactive variables.

Because this body side research project investi-gated process variation under production condi-tions, only a limited number of process and mate-rial variables could be collected. More important-ly, process variables were not purposely altered.Therefore, only inferences can be made betweenstamping variation and the observed variability inprocess variables. In many of these case studies,the process and material variables were undercontrol. As a result, our findings do not necessar-ily identify those variables that could affect partvariation, but rather which variables explain thedimensional mean shifts in these case studies.

One area of this research that is often not exam-ined is the effect of process variables on meanconformance. Most of the research on reducingstamping mean biases has been directed towardmetal forming and die design. Little researchexists on eliminating mean biases once a die hasbeen made and the actual mean biases becomeknown. Even less attention has been given to non-die related influences on mean bias such as themeasurement system effects. Among the factorsthat influence mean bias include:

• Measurement System:• Clamping sequence• Clamping forces• Part locating (datum)

• Product Design:• Part geometry (size and complexity)• Part rigidity (shape and gage)• Check point location

• Process:• Press setup and control of process

variables (see above)• Changes in stamping presses

(e.g., tryout to production presses)• Material handling and storage

13

1Process Variable Effects on Sheet Metal Quality, Journal of Applied Metalworking, American Society for Metals,, July 1986.

Page 21: Automotive Sheet Steel Stamping Process Variation

14

3.0 Analysis of Stamping Variation

3.1 Mean Conformance

One of the greatest challenges in die making andstamping is minimizing mean biases for dimen-sions on stamped parts. As defined earlier, themean bias is the absolute value of the averagedeviation from nominal. Ideally, manufacturerswould produce every stamped component suchthat each dimension is, on the average, at thespecification nominal. By doing so, design capa-bility (Cpk) would be maximized for a given level ofprocess variation. Achieving minimal mean biasesin stamping also facilitates the “tune-in” of assem-bly tooling, which is initially designed for parts atnominal, and increases the likelihood of producingdimensionally acceptable assemblies within theshortest possible lead-time. The problem is that nomanufacturer in the world has demonstrated thecapability to produce stamped body parts withoutmean biases.

Manufacturers who have minimized their meanbiases relative to their competition appear to maintain a competitive advantage in terms of cost,quality and lead-time. To achieve lower meanbiases, manufacturers employ a combination oftechnology and applied learning and limit the evo-lution of product design to reduce uncertainty.Future product designs with uncertain formingchallenges might be subject to soft tool evaluationin order to evaluate metal forming and die designbefore production tools are machined.

Modifying hard dies, or die rework, after they havebeen machined to reduce mean biases representsone of the most difficult tasks in getting diesapproved for production. Manufacturers attempt-ing to rework dimensions to reduce mean biasesface several challenges. First, since a stampedpart has a continuous surface, reworking a die toshift one dimension may affect other areas of thepart. Many areas of a part are interdependent, sothat when one dimension changes another area

does as well, sometimes in an unpredictable way.Another difficulty is trying to rework dimensionsexactly to their design nominal. Basically, there isa limited ability to hit the nominal dimension evenafter rework. A final difficulty concerns the ability tomeasure a part and to know precisely what themean bias really is. In addition to die processing,mean dimensions also are affected by the numberand positioning of clamps in measurement fix-tures. Because of the many variables in forming apart, such as changes in stamping press variablesand steel properties, and the limited ability tomeasure sheet metal, ascertaining the precisemean bias can be very difficult - both before andafter a die change. Manufacturers often face acomplicated decision in determining when torework a die or when to allow a mean bias toremain (see Body System Analysis Project Teamreport “Event-Based Functional Build: AnIntegrated Approach to Automotive BodyDevelopment”).

3.1.1 Benchmark Comparison - Body SideOuter and Inner Panels

Figure 6 on page 15 shows a histogram for 143mean dimensions across 5 parts at Company C.Several observations may be made from thesedata:

• The distribution of mean dimensions is approxi-mately normal. Assuming the measurement sys-tem does not unfairly influence mean deviations,this finding suggests an inherent variation in theability to design and construct dies to producepart dimensions at nominal.

• The distribution of mean dimensions is centeredapproximately at zero (i.e., average mean bias is near zero). This is as expected since the distribution is normal and the die maker's targetis to have zero bias.

• Approximately 10% of mean values have a biasgreater than 1.0 mm, and about 35% have a bias greater than 0.5 mm.

Page 22: Automotive Sheet Steel Stamping Process Variation

15

In general, the amount of spread in the mean dis-tribution will vary significantly by type of part.Larger, less rigid panels like the body side outeroften have significantly more dimensions withlarge mean biases than rigid panels, or panelswith blank thickness greater than 1.5 mm.

Figure 7 below compares the mean deviations forsmaller rigid reinforcement panels at Company A,such as A and B pillar reinforcements, to larger,

less rigid panels, such as one-piece body sideouter and quarter inner. The less rigid panels havelarger mean biases and also a greater dispersionin mean deviations than the rigid panels. This isevident in comparing reinforcements to a one-piece body side, and it also occurs in comparinga one-piece body side of 0.69 mm gauge to a two-piece body side of 1.10 mm gauge as shown inFigure 8 on page 16.

45%40%35%30%25%20%15%10%5%0%

Range of Mean Deviations

% o

f D

imen

sion

s (1

43 t

otal

)

<-1.25 -1.25~-.75 -0.75~-.25 -0.25~+.25 0.25~.75 0.75~1.25 >1.25

65%: [Mean] <0.5 mm

Figure 6. Histogram of Mean Values across 5 Parts for Company C

Figure 7. Mean Conformance: Rigid vs. Non-Rigid Panels

100%

80%

60%

40%

Body Side Otr/Qtr Inr (Non-rigid) Front/Center Pillar Reinforcements (Rigid)

Range of Mean Deviations

Non-rigid: 44% [Mean] <0.5Rigid: 83% [Mean] <0.5

20%

0%

<-1.5 -1.5 ~ -0.5 -0.5 ~ 0.5 0.5 ~ 1.5 > 1.5

% o

f Dim

ensi

ons

Page 23: Automotive Sheet Steel Stamping Process Variation

Table 9 below summarizes the mean bias for thebody side outer panels at each of the automotivemanufacturers. These data suggest several gener-alizations. First, larger one-piece body side outerswith integrated quarters tend to exhibit greaterbiases than two-piece body sides. Second, manu-facturers using constrained measurement systemssuch as excess part locating clamps have signifi-cantly less mean deviations. Companies E, F, and

G use constrained measurement systems, and allhave lower biases for the body side outer panel. Inaddition, the same body side panels at CompanyB exhibited less mean bias when measuring theparts in a more constrained fixture. A major influ-ence of the constrained checking system is thatextreme mean biases, those greater than 1.0 mm,are greatly reduced.

16

Figure 8. Mean Conformance: Two-Piece Body Side Panel vs. One-Piece

80%

60%

40%

One-Piece Body Side Two-Piece Body Side

Range of Mean Deviations

One-Piece: 31% [Mean] <0.5Two-Piece: 65% [Mean] <0.5

20%

0%

<-1.5 -1.5 ~ -0.5 -0.5 ~ 0.5 0.5 ~ 1.5 > 1.5

% o

f Dim

ensi

ons

Table 9. Mean Conformance by Company

Body Side # cross car Average % Dimensions % Dimensions % DimensionsCompany Type clamps in fixture [Mean] [Mean] <.5 [Mean] >1 [Mean] > tol (t)

A Integrated Quarter 11 1.10 34% 56% 66%

B (remeasured) Integrated Quarter 14 0.73 49% 29% 39%

C Two-piece 7 0.51 65% 15% 5%

D Two-piece 8 0.88 42% 39% 39%

E Two-piece 22 0.36 74% 3% 14%

F Two-piece 16 0.31 84% 3% 39%G* Integrated Quarter 17 0.37 69% 2% 28%

* Over-constrained (excess clamps) during measuring

Page 24: Automotive Sheet Steel Stamping Process Variation

The effect of a constrained measurement systemis limited to larger, less rigid panels since addi-tional clamps beyond 3-2-1 on rigid parts withgauge greater than 1.5 mm have little or no effect.Table 10 below compares mean conformance

across several part types. Although the clampingstrategy may be correlated with mean bias in thebody outer panels, the same cannot be done forrigid panels.

17

Table 10. Mean Bias by Type of Part

Body Side Non-Rigid Rigid Inner Body Side Non-Rigid Rigid InnerOuter Inner Panels Panels Outer Inner Panels Panels

Average Average Average % Dimensions % Dimensions % DimensionsCompany [Mean] [Mean] [Mean] [Mean]>1 [Mean]>1 [Mean]>1

A 1.10 0.79 0.22 56% 30% 1%

B 0.90 0.56 no data 33% 16% no data

C 0.51 0.31 0.34 15% 0% 3%

D 0.88 0.32 0.27 39% 0% 0%

E* 0.36 0.35 no data 3% 0% no data

F* 0.31 0.38 no data 3% 17% no data

G* 0.37 0.39 no data 2% 6% no data

* Over-constrained (excess clamps) during measuring

3.1.2 Mean Bias and Part Tolerances

Another contrast across manufacturers is theassignment of part tolerances. When comparingmanufacturers, the same physical dimension on abody side may have a tolerance of +/- 0.3 mm atone manufacturer and +/- 1.25 mm at another.Table 11 on page 18 shows the typical tolerancefor the body side panel and the percentage ofdimensions whose mean biases exceeds the tol-erance limit. On average, more than 30% of thedimensions in companies A through G have theirmean bias outside the tolerance. It is important tonote that whenever the mean bias exceeds the tol-erance limit, at least 50% of the panels have thatdimension outside of tolerance. It is clear that asignificant number of vehicles are being producedwith acceptable final body quality, but with a sig-nificant number of body panel dimensions out oftolerance.

Another observation across study participants isthat although Companies A through C use Cpk astheir principal buyoff criteria, they do not achievegreater mean conformance. In fact, it might beargued that the use of Cpk at Company C has ledprimarily to wider tolerances to achieve greaterCpk conformance, not greater mean conformance.Another finding is that only those manufacturersusing constrained measurement systemsassigned tolerances less than ±0.70 mm.Company F assigns the tightest tolerance at ±0.3,but uses a constrained measurement system andalso has a two-piece body side which tends tohave lower mean bias than the larger one-piecedesign.

Page 25: Automotive Sheet Steel Stamping Process Variation

18

3.1.3 Benchmark Comparison - Tryout versusProduction

Many manufacturers apply common dimensionalvalidation procedures and criteria to all body pan-els even though the expected mean bias differs bytype of panel, whether rigid versus non-rigid orsmall/simple form versus large/complex form.Table 12 below depicts mean conformance acrossmultiple parts during regular production atCompanies A, B and C. These data suggest thatmanufacturers produce stamped parts with 50-70% of dimensions within 0.5 mm. Comparing

these findings with mean biases experienced atproduction buyoff, the data are consistent.Although the other four manufacturers did not pro-vide tryout data, discussions with their personnelsuggest that their mean conformance distributionsin production also corresponded to die tryout. Themain point is that even though manufacturers mayadjust some mean biases to correct build con-cerns, the overall ability to produce mean dimen-sions at nominal does not significantly changefrom die tryout.

Table 11. Mean Conformance and Tolerances

Body Side Typical # cross car Average % Dimensions % DimensionsCompany Type tolerance clamps in fixture [Mean] [Mean] >tol (t) Cpk > 1.33

A Integrated Quarter +/- 0.7 11 1.10 66% 15%

B Integrated Quarter +/- 0.7 14 0.73 39% 80%

C Two-piece +/- 1.25 7 0.51 5% 75%

D Two-piece +/- 1.0 8 0.88 39% 23%

E Two-piece +/- 0.5 22 0.36 14% 43%

F Two-piece +/- 0.3 16 0.31 39% 29%G Integrated Quarter +/- 0.5 17 0.37 28% 37%

Table 12. Summary of Mean Bias: Tryout vs. Production (Case Study Parts)

Company % Dimensions % Dimensions % Dimensions % Dimensions[Mean]<0.5 [Mean]<0.5 [Mean]>1 [Mean]>1

A 59% 63% 26% 22%

B 51% 53% 15% 23%

C 64% 66% 13% 10%

Tryout Production Tryout Production

3.1.4 Mean Bias Stability over Time

Another important consideration regarding meanbias concerns its stability over time. Most automo-tive manufacturers first evaluate mean bias duringdie source tryout. A decision is eventually made tomove the die to the production press, oftenreferred to as the “home line”, where another esti-mate of the mean bias is made. Finally, as the diesare repeatedly run on the home line for production,

each die setup provides another opportunity toestimate the mean bias. Most of the data in thisstudy was collected during production, a year ormore after the dies were initially brought to thehome line. An important question affecting dimen-sional validation is how does the estimate of meanbias change from tryout to the home line and thento future production.

Page 26: Automotive Sheet Steel Stamping Process Variation

19

Table 13 below examines changes in part dimen-sional means between die source tryout and homeline tryout. The first two data sets are based on thecase study parts at two of the manufacturers. Twomore extensive studies of die source to home linemean shifts are also included. These data suggestthat approximately 30% of dimensions shift at least0.5 mm when the dies are moved from the tryoutpresses to the home line. The amount and uncer-

tainty of change is one reason manufacturers rec-ognize that it is necessary to re-evaluate thedimensions on a part when the dies are trans-ferred to the home line. Interestingly, a similarnumber of dimensions shift toward nominal asopposed to away from nominal, or the shift inmean bias from tryout to the home line appearsrandom.

Table 13. Comparisons of the Change in Mean Bias from Tryout to Home Line

Company # of Parts/ Median Shift [Die source % Dimensions of the Dimensions # Dimensions mean. Home line Mean] [Mean Shift] >0.5 with shift >0.5

% closer % away

B-1 4/104 0.30 30% 40% 60%

C-1 5/86 0.20 25% 68% 32%

C-2 47/652 0.23 30% 63% 37%

C-3 26/182 0.27 28% 50% 50%

Overall 0.25

Die Source to Home Line

Figure 9 below compares the mean dimension attime of part approval versus the production meanapproximately one year later at Company C. Forthe parts in this study, nearly 50% of the dimen-sions shifted more than 0.5 mm over the life of the

program. Table 14 on page 20 shows further thatof the dimensions with significant mean differ-ences, a similar number shifted closer to nominalthan away from nominal.

Figure 9. Correlation of Mean at Part Approval vs. Production Mean

2.00

1.50

1.00

0.50

0.00

-0.50

-1.00

-1.50

-2.00

-2.00 -1.00 0.00 1.00 2.00

Correlation, R = .12

Home Line Mean at Part Approval

Pro

du

ctio

n M

ean

Page 27: Automotive Sheet Steel Stamping Process Variation

These findings suggest that automotive bodyparts continue to evolve from die source tryout,through home line tryout, and even through regu-lar production. Although some of these dimension-al changes are intentional based on die rework tocorrect a problem, the majority are not. They shiftbecause of lack of process control or die rework ormaintenance in another related dimensional area.

Interestingly, some dimensions shift away fromnominal with no apparent impact on the assemblyprocess. In addition, some dimensions may shiftsignificantly closer to nominal, greater than 1 mm,but with an adverse affect on the final assembly.Figure 10 below depicts a stamping dimensionthat shifts 1.3 mm between stamping runs four andfive. Even though this shift is toward nominal, the

variation observed in assembly actually becomeshigher.

In this example, maintaining a stable mean overtime appears more important than the magnitudeof the mean deviation. Similar to stamping,assembly processes evolve over time to matchstamping mean deviations. If these mean devia-tions change significantly, assembly processeswill likely experience problems. Thus, manufactur-ers must develop a better understanding of how tominimize mean instability. Fortunately, mean insta-bility is not inherent to a process like part-to-partvariation, rather it is caused by some special influ-ence such as a process variable change or dierework. Thus, a potential exists to control thesespecial causes.

20

Table 14. Change in Mean from Home Line to Long-term Production

Company Median Shift % Dimensions % of the Dimensions [Home line mean- [Mean Shift] >0.5 with shift >0.5

Production] % closer % away

A 0.48 48% 41% 59%

B 0.57 53% 45% 55%

C 0.35 40% 44% 56%

Home Line Approval Mean to Production Mean

Figure 10. Effect of Stamping Mean Shift on Body Side Assembly(Note: above dimension is coordinated between stamping and assembly)

4.5

1 2 3 4

Stamping

Stamping .30.24

.19

.80

run 1-4 run5-6

1.3 mm mean shift

Assembly

Assembly

5 6

Mea

sure

men

t, m

m

Stamping Run #

43.5

32.5

21.5

1

00.5

Page 28: Automotive Sheet Steel Stamping Process Variation

21

3.1.5 Impact of Shipping on Mean Bias

One final investigation into the factors influencingmean bias looked at the impact of material han-dling, including:

• Racking of parts and container design for con-sistency and impact resistance,

• Time lag as stressed parts become stressrelieved and

• Movement of parts - including manual, forklifts,and truck and rail mass transit, all of whichimpact the distortion of parts through vibration.

An experiment was performed at Company Awhere four inner panels were measured bothbefore and after shipment. The measurement sys-tem used the same locating fixtures and CMM pro-grams at both the production and assemblyplants; however, different operators performed theactual measurements. The panels were shipped intheir specified containers, via truck over severalhundred miles. Two small parts were dropped intobins, one larger wheelhouse outer was stackedand the fourth part, the quarter inner, was shippedin a special rack. The results are shown in Table 15below.

Table 15. Summary of Panels Measured Before and After Shipping

Number of Average Mean % of Dimensions WithPart Check Points Bias Shift (mm) Mean Bias Shift>0.2mm

Wheelhouse Outer 69 0.89 76%

Quarter Inner 91 0.10 15%

B-pillar Reinforcement 59 0.16 19%

A-pillar Reinforcement 70 0.08 6%

This experiment indicates a potentially significantimpact of shipping on mean bias. It should benoted, however, that the impact of shipping is con-founded by different measurement operators.Reproducibility is a potential source of gage errorin this study because the same operator did notmeasure the panels before and after shipping.However, reproducibility of a CMM based on anautomatic program is generally insignificant.

The wheelhouse outer suffered the greatest meanshift with an average change of 0.89 mm. Thewheelhouse panels at the bottom of the stack hadthe largest dimensional differences, reflecting theaccumulated weight effect. For the quarter inner,special racks are used which clearly help reducethe shipping effect. The more rigid panels alsoexperienced less shipping impact, with mostmean dimensions shifting less than 0.2 mm.Similar to the die source tryout to home line analy-sis, the direction of the mean shift appears ran-dom, or equally likely to get closer to or furtheraway from nominal.

3.2 Stamping Process Variation

3.2.1 Benchmark Comparison - Part-to-PartVariation

Part-to-part or short-term variation is a measure ofthe inherent variation for a particular product, orset of dies, and process, or stamping press line.Key variable set-up parameters, such as shutheight, lubrication, cushion pressure, etc, andincoming steel coils or blanks are presumed to beconstant or consistent. Several variables mayexplain differences in part-to-part variation acrosscompanies. Some of these differences were inves-tigated, including:

• measurement and clamping system,• check point location/axis on the part, and• part rigidity, size and material thickness.

Table 16 on page 22 summarizes part-to-part vari-ation for the body side outer panels for each of themanufacturers studied. A comparsion of variationacross manufacturers is again difficult because of

Page 29: Automotive Sheet Steel Stamping Process Variation

the different measurement strategies as demon-strated by the number of clamps. The three manu-facturers using the most clamps (E, F, and G) havethe lowest part to part variation. In addition, part topart variation at Company B is significantly lowerwhen using a more constrained measurement sys-tem. These data suggest that adding measure-

ment clamps will likely reduce the observed part to part variation for large, non-rigid parts. Both the average and the extreme variation points, or 6

part-to-part greater than 1.5 mm, appear to be sig-nificantly reduced by the additional secondarylocating clamps.

The type of body side, one-piece versus two-piece, also appears to affect variation. CompaniesA through D use roughly the same number ofclamps, but have two different body side styles,integrated quarter panel and two-piece. It appearsthat the two-piece body side results in lower aver-age part-to-part variation than the larger and morecomplex integrated quarter body side by about10%. The same relationship is seen among com-panies E, F, and G using the more constrainedmeasurement approach. Of these, company Gwith the larger body side has the highest 95th per-centile part to part variation. Follow-up analysis atcompany G indicates that most of their high varia-tion dimensions are in non-stable measurementareas in the tail area of the body side outer panel.

Since part-to-part variation differs according tobody side style, it would be expected to varyaccording to part size and rigidity for non-bodyside outer panels. The panels in these case stud-ies may be grouped into three categories: bodyside outer panel, non-rigid body side inner panels

and rigid body side inner panels. The body sideouter panel is the largest and one of the lightestgauge panels, varying from 0.69 mm to 0.90 mmthickness. The non-rigid body side inner panelsare arbitrarily limited to 1.5 mm thickness and aresmaller than the outer panel. These panels includethe quarter inner, wheelhouse outer and roof rails.The third category consists of small, heavy-gaugeparts, including the A- and B-pillar reinforcements.

Figure 11 on page 23 plots the average standarddeviation, or sigma, for all parts studied at theseven manufacturers. The changes in thesegroupings from large and flimsy to smaller and/ormore rigid can be seen to correlate with the aver-age amount of part-to-part variation. As panelsbecome smaller and more rigid, their part to partvariation decreases. In addition, Figure 11 sug-gests that the body side panels with the lowestvariation are from manufacturers using moremeasurement clamps, thus masking some of theactual process variation.

22

Table 16. Part-to-Part Variation for Body Side Outer PanelsNote: 95th percentile is the level of variation where 95% of the dimensions on the part are less than this amount.

Body Side # cross car Average 95th Percentile % DimensionsCompany Type clamps in fixture 6 part-part 6 part-part 6 part-part >1.5

A Integrated Quarter 11 1.14 2.90 20%

B (remeasured) Integrated Quarter 14 1.09 2.35 23%

C Two-piece 7 0.99 1.89 18%

D Two-piece 8 0.99 1.57 10%

E* Two-piece 22 0.48 0.81 0%

F* Two-piece 16 0.32 0.50 0%G* Integrated Quarter 17 0.40 1.08 0%

* Over-constrained (excess clamps) during measuring

Page 30: Automotive Sheet Steel Stamping Process Variation

23

Another difference among manufacturers is thenumber and location of dimensions measured.Two manufacturers, companies D and E, collectless data on their stamped panels than the othermanufacturers, and primarily collect data frompoints located in more rigid localized part areas.Although a body side outer panel tends to be flim-sy, certain areas in highly formed sections of thepart, such as the door openings, are typicallymore rigid than the tail or wheelhouse areas.Control of these more rigid areas often is moreimportant than other areas because they are lesslikely to conform to reinforcements during assem-bly. As has been shown, dimensions on less rigidparts tend to have greater variation. In order to

illustrate the impact of dimension location, Table17 below shows the body side variation for com-panies D and C. At company D, 24% of their bodyside dimensions have an average standard devia-tion greater than 0.2 mm. Company D measuresnear the A- and B-pillars and on the flanges in thedoor openings. In contrast, company C measuresdimension throughout the body side and has 73%of their dimensions exceeding 0.2 mm. However,when comparing dimensions in similar locations,the variability at company C more closely resem-bles company D. Thus, the expected variation ona stamped panel appears dependent upon wherethe dimension is located and how rigid the part isat that location.

Figure 11. Average Variation (Standard Deviation) by Type of Part

0.30

0.25

0.20

0.15

0.10

0.05

0.00

integratedquarters

Body Side Non-Rigid Rigid (guage>1.5)

Ave

rag

e

par

t-p

art

small,simpleE, F, G

6 =1

Table 17. Effect of Dimension Location on Variation

SelectedCompany Dimension <0.2 >0.2

D 14 76% 24%

C 40 27% 73%

C 14 (common with D) 60% 40%

Page 31: Automotive Sheet Steel Stamping Process Variation

3.2.2 Variation Over Time

In theory, part to part variation produced from a setof dies on the same press line should remain con-stant over time. In practice, part-to-part variationdoes vary for some dimensions. Variables thatmay affect part-to-part variation over time include:

• The condition of the press line, a function of thelevel of maintenance of the presses,

• The condition of the dies, a function of die main-tenance and engineering change rework, and

• Processing variables, such as the control ofcushion pressure, material handling, automationbetween presses, etc.

Although many of these changes often are associ-ated with mean shifts, part-to-part variation can beaffected as well. Table 18 below shows that part-

to-part variation typically increases from partapproval runs to regular production. These datasuggest that the average six sigma increases from 0.8 mm to 1.2 mm after more than a year in pro-duction. The most likely explanation for this differ-ence is that operating conditions at buyoff aresubstantially more controlled than in regular pro-duction. Although the overall variation increases,not every dimension exhibits an increase. Figure12 below compares the observed part-to-partstandard deviation at buyoff versus regular pro-duction. This illustration indicates a general lack ofcorrelation between part approval variation andregular production. For some dimensions, the vari-ation increases and for others it decreases,although more dimensions have higher part to partvariation in production.

24

Table 18. Part-to-Part Variation: Home Line Approval vs. Production by CompanyNote: production data 1 year + after home line buyoff

Company # Parts Average Average % Dimensions % Dimensions(# Dimensions) 6 part-part 6 part-part 6 part-part>1 6 part-part>1

A 1 (37) 0.79 1.16 14% 48%

B 5 (132) 0.96 1.32 26% 48%

C 39 (327) 0.84 1.14 23% 38%

Home Line Production Home Line Production

Figure 12. Part-to-Part Variation: Home Line Tryout Approval vs. Production by Dimension

0.80

0.60

0.40

0.20

0.000.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80

Correlation, R = .21

part-part – Home Line Tryout Approval

par

t-p

art–

Pro

du

ctio

n

Page 32: Automotive Sheet Steel Stamping Process Variation

25

3.2.3 Impact of Shipping on Variation

As previously mentioned, part shipping from thestamping plant to the assembly plant caused sev-eral mean dimensions to shift, particularly for thenon-rigid wheelhouse outer panels. This sectionexamines the effects of shipping on variation. Asnoted earlier, some potential operator noise existsbecause different operators measured the panelsbefore and after shipping. However, this operatoreffect is unlikely to be significant, as the panels

were measured using the same fixtures and auto-mated CMM programs.

Table 19 below indicates that part-to-part variationincreased on 87% of the dimensions for the fourparts: wheelhouse outer, quarter inner, A-pillarreinforcement, and B-pillar reinforcement, with alesser increase on the more rigid components, theA- and B-pillar reinforcements. Clearly, part ship-ment increases part-to-part variation.

Table 19. Summary of Remeasured Data Before and After Shipping (via truck)

Panel Measurement Points Variation Increased

Wheelhouse Outer 69 91%

Quarter Inner 91 92%

B-Pillar Reinforcement 59 86%

A-Pillar Reinforcement 70 76%

3.2.4 Components of Variation: Part-to-Part,Run-to-Run, and Begin-End of Run

Stamping variation may be broken down into threecomponents of variation: part-to-part, run-to-run,and begin-end of run (see Section 2.0). Total vari-ation, total, is a statistical summation of these three

variation components. One reason for looking atthe components of variation individually is that theeach is a reflection of different root causes. Table20 below shows the part-to-part and total variationfor each auto company's body side outer panel.

Table 20. Summary of Part-to-Part and Total Variation for the Body Side Outers

Body Side # cross car Average Average % DimensionsCompany Type clamps in fixture 6 part-part 6 total 6 total >1.5

A Integrated Quarter 11 1.14 1.41 29%

B Integrated Quarter 14 1.09 1.93 57%

C Two-piece 7 0.99 1.88 42%

D Two-piece 8 0.99 1.21 23%

E Two-piece 22 0.48 0.52 0%

F Two-piece 16 0.32 0.49 0%G Integrated Quarter 17 0.40 0.77 3%

Page 33: Automotive Sheet Steel Stamping Process Variation

Companies E, F, and G, which used the most con-strained measurement systems at 16, 17, and 22clamps respectively on the body side outer panel,have the lowest part-to-part and total variation.Comparing companies C and D, excluding theclamping effect, showed that although the twomanufacturers exhibit similar part-to-part variation,company C has much higher total variation. Figure13 below shows that company C has significantlymore run-run and begin-end of run mean shifts.Thus, company C does not appear to control theirprocess as well as company D. A similar finding isobserved in comparing companies A and B with

company D. Among companies E, F, and G, theconstrained measurement companies, companyG appears to have less control over their meanshift variation. In general, manufacturers with sim-ilar panels and similar checking systems shouldhave similar levels of dimensional variation. Whenthey do not have similar levels of variation, the dif-ference typically is not related to inherent part-to-part variation, but rather to how well one manufac-turer controls its process over time.

Table 21 on page 27 shows the amount of variationfor each of the parts studied at company A, bysource of variation. The sample size for each typeof panel is 36 right and 36 left, or 72 total for eachtype. The numbers expressed in Table 21 areaverages across all the dimensions on a part andtherefore are non-additive. These data indicatethat less rigid panels exhibited the largest part-to-part and mean shift variation. Interestingly, thevariation for a particular component is not always

the same for right and left mirror image parts. Atcompany A, the right hand body side outerexhibits significantly less variation than the leftside. Overall, variation at company A is relativelylow with the exception of the left body side.Although part-to-part variation is typically largerfor a one-piece body side, the principal reasonthat the left side has significantly higher variationthan the right side is due to mean shifts betweenstamping runs.

26

Figure 13. Components of Variation for Body Side Panel at Company C and D(Note: total is greater at Company C due to mean shifts not part-part variation)

% o

f To

tal O

bse

rved

Var

iati

on

Part-part Run-run Begin-end of run

Company C Company D

80%

100%

60%

40%

20%

0%

90th Percentile part-part C: 0.27 D: 0.2490th Percentile total C: 0.47 D: 0.29

46%

35%

20%

71%

Page 34: Automotive Sheet Steel Stamping Process Variation

27

Table 22 below shows the percentage of total vari-ation at company C according to variation source:part-to-part and mean-shift (run-run and/or begin-end). The effects of mean shifts at company C aremore significant than company A. The variation ofthe body side, front pillar and center pillar rein-forcements are approximately doubled due tomean shifts. An analysis of the roof rail and wind-shield frame suggests one potential challenge in

assessing mean shifts. Because analysis of vari-ance methods are used to estimate mean shiftvariation, higher part-to-part variation will maskmean shift variation. In other words, the true meanshift variation cannot be effectively evaluated if theinherent variation is unstable, a violation of thehomogeneity of variance assumption used inANOVA models.

Table 21. Sources of Variation by Part for Company A

Average Average Average Average % of VariationPart run-run begin-end part-part total Explained by

Mean Shifts

Body Side - RH – 0.15 0.19 0.24 31%

Body Side - LH 0.26 0.15 0.26 0.34 43%

Quarter Inner 0.07 0.07 0.08 0.10 43%

Wheelhouse Outer 0.11 0.09 0.09 0.14 50%

B-Pillar Reinforcement 0.04 0.07 0.05 0.08 59%

A-Pillar Reinforcement 0.06 0.05 0.07 0.08 28%

Table 22. Sources of Variation by Part for Company C

Average Average Average % of VariationPart mean-shift part-part total Explained by

Mean Shifts

Body Side - RH 0.26 0.17 0.31 79%

Roof Rail 0.23 0.28 0.34 32%

Front Pillar Upper 0.16 0.09 0.18 76%

Front Pillar Lower 0.15 0.09 0.18 76%

Center Pillar 0.21 0.08 0.23 92%

Windshield Frame 0.15 0.20 0.22 22%

3.2.5 Steel Properties and Press SetupControl and Stamping Variation

These case studies under production conditionsprovide an opportunity to investigate possible rootcauses of mean shift variation. Short-term or part-to-part variation is assumed to result from severalfactors related to product design, part size andrigidity, die design, stamping press condition orthe measurement system. Mean shift variation run-

to-run and within run, however, is generally relatedto changes in the process over time, such as therepeatability of press setup or changes to materialproperties. Although this study did not provide anopportunity to rigorously control variables toascertain direct cause and effect relationshipsbetween process input variables and variation, itdoes allow for some general conclusions regard-ing the causes of mean shifts.

Page 35: Automotive Sheet Steel Stamping Process Variation

Five manufacturers collected input data for bothprocess and material variables across thirty parts.(companies E and F did not participate). They col-lected this data for each sampling of three panels,or, in some cases, once per run. The materialcoupons were analyzed later, either at an inde-pendent test laboratory, three participants, or in-house, two participants. The following variableswere collected when possible:

• Process data (at each setup)- Draw press shut height- Draw Tonnage- Die cushion pressure (if applicable)- Outer ram tonnage (if double-action

press used)

• Material data (a steel coupon was sampledwhen a sample of parts was taken from the production run)

- Gauge- Yield strength- Ultimate strength- n-value- Percent elongation

Due to data collection limitations, it was not possi-ble to match process and material variable datadirectly to a particular panel. For example, thematerial properties of the steel for each individualpanel are unknown. Thus, the analysis is limited totrying to explain mean shift variation and not part-to-part variation. For instance, if mean shifts

account for only 20% of the total observed varia-tion, then the most variation that can be explainedwith the input variables collected is 20%. Thisanalysis only identifies relationships between control of input variables and mean shifts.

Of the thirty parts with process input data, approx-imately 33% of the dimensions, 330 out of 1135,had at least one large mean shift greater than 0.5mm over the data collection period. Thus, prior toany mean shift analysis, over two-thirds of thedimensions studied were found robust to the vari-ability of their respective process and materialinput variables.

The next step was to examine the relationshipbetween process variable control and mean-shiftvariation. Table 23 below compares mean shiftvariation with process input variation usingallowed ranges. Allowed ranges are essentially thetolerances of the process and material input vari-ables. Thus, if manufacturers control theirprocess-input variables within these ranges, theyshould not observe significant mean shifts relatedto these variables. Generic allowed ranges areused instead of tolerances to permit comparisonamong manufacturers with different process andmaterial variable specifications. Furthermore,since this analysis only looks for relative variationdifferences, the nominal or average value of eachvariable is not important.

Table 23. Summary of Product and Process Variation Compliance

% Parts within Correlation, R,Variable Robust Range Robust Range to mean-shift

Material Gauge 0.06 mm 96% 0.23

Yield Strength 6 ksi 95% 0.22

Ultimate Strength 6 ksi 92% 0.24

% elongation 9% 100% 0.19

n-value 0.04 100% 0.09

Inner Tonnage 60 tons 45% 0.69

Outer Tonnage/Cushion 50 tons/+/- 10% 48% 0.47

28

Page 36: Automotive Sheet Steel Stamping Process Variation

Table 23 shows that most steel variables, 92% to100%, fall well within their expected ranges ofvariation. Consequently, it is not surprising to seethat their correlation with mean shifts is relativelylow with values ranging from 0.09 to 0.24, where 0has no correlation, 1.0 is a perfect correlation anda value greater than 0.6 is considered correlated.In general, the steel manufacturers studied hadcontrol of their variation, and even when they didnot, material property variability could not be correlated with dimensional mean shifts.

The process variables of inner tonnage, outer ton-nage, and cushion pressure, had considerablymore variation and operated within the allowedrange only 45% to 48% of the time. The result wasa much higher correlation to mean shifts.Presumably, the opportunity for variation reductionfor these part dimensions is significant if the presssetup variable of tonnage and cushion pressurecan be controlled more tightly. Figure 14 belowsuggests an observed threshold of around 75 tons

as the limit to the allowable range of variation forcontrolling dimensional mean shifts. Note thatthese observed ranges relate only to dimensionalmean shifts and do not consider potential impactson formability issues such as splits or wrinkles.

A few additional comments with respect to thisanalysis are appropriate. First, tonnage readingsmay be affected by several setup variables suchas lubrication, die placement in press, shut height,etc., and thus the correlation to mean shifts shouldbe viewed principally as an indicator of lack ofprocess control. Second, the relationship betweentonnage and mean shifts over a continuous rangeof tonnage settings was not analyzed scientificallyfor every part. Therefore, these data should not beused to identify tonnage specifications for a par-ticular part. Rather, simply recognize that thoseparts in this study exhibiting large mean shiftstended to have relatively poor control of theprocess variables but good control of the materialvariables.

Figure 14. Relationship between Press Tonnage and Mean Shift Variation ( mean shift)

0.35

0.30

0.25

0.20

0.15

0.10

0.05

0.00

0 100 200 300 400

Range of Draw Die Tonnage

mea

n-s

hif

t

(<75)

Range 90-300

3.2.6 Effect of Mean Shifts on StatisticalProcess Control Techniques

All manufacturers in the benchmark study exhibit-ed some level of mean shift variation for the major-ity of their part dimensions as shown in Table 24 onpage 30. Of the 1287 dimensions examined,approximately 80% would have at least one sub-

group plot out-of-control on an X-bar chart.However, only 20% of the dimensions had a meanshift greater than 0.5 mm. Note that the majority ofthese mean shifts occurred on the parts at companies B and C. Again, these mean shiftslargely explain why certain manufacturers havemore variation in their process than others.

29

Page 37: Automotive Sheet Steel Stamping Process Variation

The fact that such a large percentage of dimen-sions would plot out-of-control on an X-bar charthas serious implications for process control. Oneinterpretation is that stamping and die processesby nature are not stable enough to produce partswith stable mean dimensions, even at world-classfacilities. Another interpretation is that the part-to-part variation of a stamping process often is so lowthat even well maintained processes will exhibitsome process drifts over time. Assuming, forexample, that the inherent standard deviation of astamping process is 0.10 mm, a process will bedeemed statistically out-of-control if a mean shiftsby more than 0.15 mm(2). Most manufacturerswould not want to adjust a process for a 0.15 mmmean shift.

Assuming that small mean shifts are inevitablewith the die changeover process, the traditionaluse of X-bar charts to assess mean stability maybe unnecessarily stringent. The small part-to-partvariation results in tight control limits, and this inturn results in many out-of-control dimensions.Since small mean shifts rarely affect assemblybuilds, manufacturers using control charts oftenignore the results. This is true even if larger shifts

are observed. The main concern with X-bar/Range charts for stamping is that they do noteffectively separate problems from insignificantprocess changes. One approach to desensitizecharts is to replace X-bar/ Range charts withIndividual and Moving Range charts.

Individual and Moving Range charts are based onsubgroup sizes of one. Control limits to assessmean stability are then based on moving ranges.Because moving range values are based on con-secutive subgroups, variation estimates reflect thepart-to-part variation and some mean shift varia-tion. Table 25 on page 31 presents process controldata for a stamping dimension. Using traditional X-bar charts, this process would be consideredunstable or out-of-control as shown in Figure 15,on page 32. Interestingly, if only the first observa-tion in each subgroup is measured and Individualand Moving Range charts are used, this sameprocess would be deemed in control. The reasonis that Individual charts based on moving rangesare less sensitive than X-bar charts if small meanshifts are inherent to the process. Of course, withindividual and moving range charts, large signifi-cant mean shifts may still be identified.

Table 24. Summary of Mean Shift Variation across Companies

# of Average % Dimensions w/ % DimensionsCompany Dimensions total Significant Mean Shift [mean shift> .5]

A-RH 329 0.12 80% 3%

A-LH 282 0.15 88% 12%

B 262 0.36 80% 51%

C 143 0.28 84% 31%

D 62 0.19 85% 3%

E 41 0.09 34% 0%

F 61 0.10 82% 3%

G 107 0.15 82% 14%

Total 1287 0.18 81% 19%

2The control limit for an X-bar chart is equal to A2(n) x d2(n) x part-part, where A2 and d2 are functions of subgroup size. If the sub-group size, n, is equal to 4, then the control limits are +/-0.729 x 2.059 x 0.1 or +/- 0.15mm.

30

Page 38: Automotive Sheet Steel Stamping Process Variation

Table 25. Process Control Data

Subgroup Sample Sample Sample Sample X-bar Range X Rm(i) 1 2 3 4 (i) (I) (I=2) (I)

1 0.40 0.30 0.20 0.50 0.35 0.30 0.30 0.00

2 0.25 0.50 0.40 0.30 0.36 0.25 0.50 0.20

3 0.25 0.25 0.05 0.15 0.18 0.20 0.25 0.25

4 0.50 0.20 0.10 0.20 0.25 0.40 0.20 0.05

5 0.90 0.75 0.85 0.70 0.80 0.20 0.75 0.55

6 0.65 0.40 0.50 0.90 0.61 0.50 0.40 0.35

7 0.20 0.40 0.25 0.25 0.28 0.20 0.40 0.00

8 -0.10 0.10 0.25 0.20 0.11 0.35 0.10 0.30

9 0.25 0.30 0.30 0.25 0.28 0.05 0.30 0.20

10 0.40 0.25 0.10 0.20 0.24 0.30 0.25 0.05

11 0.40 0.65 0.50 0.30 0.46 0.35 0.65 0.40

12 0.30 0.25 0.20 0.25 0.25 0.10 0.25 0.40

13 0.10 0.10 0.00 0.10 0.08 0.10 0.10 0.15

14 0.40 0.30 0.70 0.50 0.48 0.40 0.30 0.20

15 0.30 0.25 0.30 0.30 0.29 0.05 0.25 0.05

16 0.35 0.60 0.50 0.40 0.46 0.25 0.60 0.35

17 0.15 0.15 -0.05 0.05 0.08 0.20 0.15 0.45

18 0.60 0.30 0.20 0.30 0.35 0.40 0.30 0.15

19 0.70 0.55 0.65 0.50 0.60 0.20 0.55 0.25

20 0.75 0.60 0.90 1.00 0.81 0.40 0.60 0.05

21 0.15 0.20 0.35 0.40 0.28 0.25 0.20 0.40

22 0.30 0.50 0.25 0.60 0.41 0.35 0.50 0.30

23 0.15 0.20 0.20 0.15 0.18 0.05 0.20 0.30

24 0.30 0.55 0.40 0.50 0.44 0.25 0.55 0.35

25 0.75 1.00 0.85 0.65 0.81 0.35 1.00 0.45

Average 0.38 0.26 0.39 0.25

31

Page 39: Automotive Sheet Steel Stamping Process Variation

The use of Individual and Moving Range charts forstamping processes solves the problem of over-sensitive control charts; however, it does not nec-essarily result in better process control. The fun-damental problem with statistical process controlcharts for stamping is that they merely exposemean shifts. Effective process control requires anunderstanding of the robustness of dimensionalmeasurements to input variables and then the dis-cipline to control the variation within these robustlevels. For example, manufacturers should identifysafe operating windows for draw tonnage, cushionpressure, shut height, counterbalance pressure,air pressure, n-value, material thickness etc. They

then need to operate their processes within thesewindows. If they can meet this objective, there islittle need to measure stamped parts during regu-lar production. However, many manufacturerseither have insufficient knowledge of the robust-ness of their processes to input variables or arenot consistent in monitoring them.

Ultimately, whether a non-stable mean is accept-able depends on the influence that the variationwill have on the assembly. In these case studies,most assembly dimensions were robust to the vari-ability of their coordinated stamping dimensions.

Figure 15. X-Bar/Range Chart vs. Individuals/ Moving Range Charts(Note: charts based on the same process data)

0.900.800.700.600.500.400.300.20

subgroup #

x bar Chart

UCL

CL

LCL0.100.00

1

xbar

(i)

3 5 7 9 11 13 15 17 19 21 23 25

1.21

0.80.60.40.2

0

subgroup #

Individuals Chart

UCLx

CL

LCLx-0.2-0.4

1

Indi

vidu

als,

xi

3 5 7 9 11 13 15 17 19 21 23 25

0.70

0.60

0.50

0.40

0.30

0.20

subgroup #

Range Chart

UCL

CL

LCLR

Rbar

R

0.100.00

1

xbar

(i)

3 5 7 9 11 13 15 17 19 21 23 25

0.90.80.70.60.50.40.3

subgroup #

Moving Range Chart

UCL

CL

LCL

Rm

Rm

0.10.2

0

1

Mov

ing

Ran

ge

3 5 7 9 11 13 15 17 19 21 23 25

32

Page 40: Automotive Sheet Steel Stamping Process Variation

Table 26 below indicates that relatively few dimen-sions, less than 5%, exhibited strong correlation.Although stamping-to-assembly correlation is low,some stamping dimensions with mean shiftsgreater than 0.5 mm corresponded with assembly

dimensions demonstrating higher variation. Thus,the elimination of large stamping mean shiftswould likely lead to a reduction in some assemblyvariation.

Table 26. Effect of Stamping Mean Shifts on Assembly Variation

# Dimensions with # Dimensions withCompany # Coordinated Significant Stamping Mean Median assembly Median assembly

Dimensions Correlation Shift > 0.5 if shift < .5 if shift < .5

A 33 1 1 0.18 0.30

B 104 8 62 0.16 0.23

C 32 2 14 0.19 0.38

D 31 0 1 0.22 0.21

E 32 0 0 0.16 none

F 8 2 0 0.20 none

G 77 1 9 0.13 0.13

Totals 317 14 (4%) 87 (27%) Average=0.16 Average=0.25

Effect of Stamping Mean Shifts

33

Page 41: Automotive Sheet Steel Stamping Process Variation

4.0 Tolerance Considerations

4.1 Tolerances

Two objectives for assigning sheet metal toler-ances are to help insure that final assembly quali-ty will be met and to minimize productivity lossesduring assembly because of large stamping varia-tion. Assigning tight tolerances help achieve thisgoal. The tradeoff to assigning overly tight toler-ances, however, is that die and stamping costsmay become excessive trying to meet them. Insome respects, the tolerance has the effect ofshifting costs from stamping to assembly or fromassembly to stamping, depending on the toler-ance assigned. A reasonable and meaningfulsheet metal tolerance needs to consider the fol-lowing three factors:

• Stamping process capability: The tolerance must reflect what a stampingprocess is capable of achieving, otherwiseunnecessarily high stamping costs will accrue.There are many current instances wherestamped parts are out of tolerance, but are beingassembled successfully. All of the benchmarkautomotive manufacturers had body side outerpanels with a significant number of points out oftolerance. This is evidence that manufacturerstend to assign unnecessarily tight tolerances onstamped parts, particularly for less-rigid outerpanels. When stamping plants have difficultymeeting assigned tolerances, there is a tenden-cy to overlook the tolerance and wait to hear ifassembly generates build problems. This wait-and-see approach would be improved upon ifthe tolerances were known to be meaningful.

• Impact on assembly: Unlike many other rigid assembly processes, theassembly of sheet metal affects final part geom-etry. The assembly process has the ability to addor reduce variation depending upon the compo-nents and the assembly process. Many assem-bly processes are robust to a wide range ofstamping variation showing virtually no impacton assembly quality due to stamping variation. Inthese instances, it would benefit manufacturersto widen stamping tolerances, at least to thepoint where they begin to impact assembly.

• Measurement system limitations: Because of the impact the measurement systemhas on the ability to measure stamped panels,part tolerances need to reflect the measurement

system design. It was shown earlier that meas-urement fixtures with more clamps tended tohave parts with tighter tolerances than thosemeasured with fixtures using fewer clamps. Theamount of observed variation with constrainedchecking fixtures is less than that of less-con-strained fixtures and therefore, tighter tolerancescan be achieved.

4.2 Cp and Cpk (Pp and Ppk)

The predominant tolerance strategy used by auto-motive manufacturers is to assign toleranceswhich may be difficult to achieve but are believedto help final assembly quality while reducingassembly problems. In some cases, overly tighttolerances are assigned; if not readily achieved,they can be re-evaluated during development. Anadvantage of this strategy is that certain partswhere all the tolerances are met are approvedwithout special intervention. One concern with thisstrategy, however, is that many dies are unneces-sarily reworked to meet the original toleranceseven though they may not impact assembly build.This unnecessary rework leads to delays. Sincethese manufacturers often use process capabilityindices to measure conformance to tolerance,they will be discussed next.

Two process capability indices often used to com-pare how well a process is achieving the designtolerances are Cp, process capability, and Cpk,design capability. These indices are a function ofthe tolerances, part-to-part variation and meanbias, and were developed to measure the capabil-ity of a process relative to design intent. The for-mula for Cp is:

Equation 5 Cp = USL - Nominal3 part-part

The Cp index is determined by dividing one half ofthe tolerance, where one half the tolerance equalsthe upper specification limit (USL) minus the nom-inal, by three standard deviations of part-to-partvariation. The formula for Cpk is:

Equation 6 Cpk = USL - Mean Bias3 part-part

(Note: mean bias = process mean - nominal)

34

Page 42: Automotive Sheet Steel Stamping Process Variation

The Cpk index is determined similarly to Cp, exceptthat any mean bias is first subtracted from thenumerator. If there is no mean bias and theprocess is operating exactly at the design nomi-nal, then Cp = Cpk. For the purpose of these cal-culations, part-to-part is estimated using statisticaltables and the formula:

Equation 7 part-part = R d2

If the sample standard deviation is used to esti-mate part-to-part rather than the above formula, thenthe Cp and Cpk indices are referred to as Pp andPpk. Their interpretation, however, is the sameregardless of the method used to estimate part-to-

part. Figure 16 below illustrates differences in Cp

and Cpk for three different scenarios.

Figure 16. Illustration of Cp and Cpk calculations for three scenarios

Mean

Mean

Mean

nominaltolerance

nominaltolerance

nominaltolerance

0.5

Tol ±1.0 ±1.0 ±1.0

0.25 0.33 0.25

Cp 1.33 1.0 1.33

Cpk 1.33 1.0 0.67

4.3 Recommended Tolerances for Sheet Metal

The tolerance guidelines shown in Table 27 onpage 36 are based on these empirical benchmarkstudies. These guidelines allow consideration forprocess capability, or achieving a Cp = 1.33, influ-ence on assembly dimensions and measurementsystem limitations. They also assume that the datais obtained without over-constrained measurementsystems. Furthermore, these tolerances onlyreflect manufacturing variation about the long-termprocess mean, and do not consider the ability tohit the design nominal. Since dimensions routinelydeviate from design nominal, initial specificationsmay account for both mean bias and processvariation, resulting in wider tolerances than thoseshown in Table 27.

These case studies also suggest that rigid com-ponents, typically with material gauges greater

than 1.5 mm, have greater process capability, orsmaller variation, exhibit more influence on theassembly, and therefore warrant smaller toler-ances. Rigid components also tend to exhibitgreater repeatability from die set to die set, so bothshort-term and long-term tolerances are smallerthan other components. Dimensions for non-rigidpanels are divided into two groups; mating sur-faces and non-mating surfaces. Mating surfacesoften are more critical for assembly, and thus mayhave tighter tolerances than non-mating surfaces.In all cases represented in Table 27, a tolerancerange is shown because the ability to control vari-ation may differ around the part. These general tol-erances are a function of the inherent sigma andassume that a Cp of 1.67 is desired. For all threecategories, manufacturers should be able to atleast meet the high end of the tolerance guidelinebased on the capability of stamping processes.

35

Page 43: Automotive Sheet Steel Stamping Process Variation

4.4 Part Tolerances and Functional Build

The assignment of part tolerances often hinges onwhether to allow for mean bias, or deviation fromnominal. The previous section recommended parttolerances based on manufacturing variation with-out consideration of mean bias. Since mean biasis not considered, the Cp index may be used tomeasure conformance to design, but Cpk is notused. This development strategy relies on twosteps: minimize variation to an acceptable leveland evaluate the impact of mean bias on theassembly to determine which points, if any, requirerework. Here, the assembly build is used to identi-fy dimensional shifts and not product specifica-tions. Several manufacturers use this functionalbuild strategy and the advantages include:

• Less die rework is needed because only dimen-sions that adversely affect the final assembly areidentified for rework.

• Development lead-time is saved because lessdie rework is required.

• Lower overall process variation is achieved bothin stamping and in assembly. Many engineersbelieve that as the amount of die rework increas-es from shifting many dimensions toward nomi-nal, the less robust the die becomes.

This functional build strategy may also helpimprove process control because the final specifi-cations for mean bias and process variation aredetermined during tryout and thus better reflectprocess capability and the influence on assembly.The consequence of not meeting the final toler-ances is better understood without waiting to hearfrom assembly.

Table 27. General Recommended Tolerances for Stamped Parts Based on Process Capability(Note: data based on measurements systems without over-constrained clamping)

Part Location of Inherent Tolerance to Achieve Cp > 1.67Rigidity Dimension Sigma (tol > 3Cp or +/- 5sigma)

rigid dimensions(~gauge > 1.5 mm) all .06 ~ .15 0.3 to 0.75

Mating Surface .10 ~ .20 0.5 to 1.0non-rigid dimensions(~gauge > 1.5 mm) Non-Mating .10 ~ .25 0.5 to 1.25

Surface

36

Page 44: Automotive Sheet Steel Stamping Process Variation

5.0 Conclusions and Summary

The following conclusions are based on the analy-sis contains in this report and from observationsmade throughout the study. Since much of thedata collection was obtained under productionconditions in a non-statistically structured manner,the analysis is not sufficiently rigorous to establishconclusive results in many areas. Due to the num-ber of product and process variables seen at asingle manufacturer, rigorous experimentationwould have severely limited the breadth of analy-sis. The following general conclusions provideinsight from several manufacturers, and reflect dif-fering design and manufacturing strategies struc-tured around common operating principles ofsheet metal design, die construction and metalforming. These conclusions also provide guide-lines to developing more rigorous researchdeemed necessary at particular manufacturerschoosing to develop a more scientific approach tostamping variation and measurement.

1. An important distinction across companies wasthe type of panel measuring system used onlarge, non-rigid parts like the body side andwheelhouse outer panels. The greater the num-ber of clamps, the less observed variation andmean biases were seen in the measurementdata. Constrained measurement systems hadbetween 16 and 22 in/out clamps, whereas thelesser-constrained systems used from 5 to 11clamps. The use of clamps and their location isindicative of different dimensional validationand process control strategies not discussed inthis report. It is important to note the differencebecause of the impact on the measurementdata for large panels. Manufacturers using con-strained measurement systems also assignedtighter tolerances to the body side. The con-strained tolerances varied from 0.3 mm to 0.50 mm, where the unconstrained tolerancesvaried from 0.70 mm to 1.25 mm.

2. There are significant differences in the amountof variation seen in larger, less-rigid parts suchas a body side outer panel versus smaller rein-forcements such as A and B pillar reinforce-

ments. Larger parts experience from 20% to500% more mean bias on the average, from 0.5 mm to more than 1.0 mm for unconstrainedmeasured parts. The amount of mean biasvaries considerably across manufacturersdepending on many factors, including meas-urement strategy, panel size and die buyoffstrategy. Large parts also demonstrate up totwice as much variation as small parts, and thevariation is distributed across part-to-part andmean shift variation.

3. Short-term variation is relatively small with the95% 6-sigma less than 1.0 mm for rigid partsand less than 2.0 mm for the body side outer,using unconstrained measuring. If the meanbias could be eliminated, many parts wouldreadily achieve a Cpk = 1.33. A significant challenge during dimensional validation is eliminating mean bias, particularly for large orsmall complex panels.

4. Large, less-rigid panels also are more suscepti-ble to changes in variation due to transferringthe dies from the tryout source to the home lineand from home line tryout to future production.In both cases, both the mean bias and theamount of variation are likely to increase. Small,rigid panels have smaller changes in variationwhen transferring from tryout presses to thehome line. In some cases during production,they show a decrease in mean bias from thehome line tryout. It is likely that die rework hastaken place during the production life to reducemean bias, and attention may have beenfocused more on the rigid panels than on thelarger ones. Small, rigid panels are also lesssusceptible to increased variation and meanbias due to shipping influences than are largerpanels. Several small panels experienced from6% to 19% of the dimensions shifting at least0.2 mm due to shipping, whereas the wheel-house outer had 76% of the dimensions shift atleast 0.2 mm. The difference in the variationincrease was not as significant, where the smallpanels averaged 85% of their dimensionsincreasing in variation and the wheelhouseincreasing 91%.

37

Page 45: Automotive Sheet Steel Stamping Process Variation

5. Manufacturers with similar part design andmeasurement systems, but with different levelsof total variation, usually experience varyingdegrees of run-to-run mean shifts. As expected,part-to-part variation is similar for manufacturerswith similar measuring strategies and productdesigns. The two setup-related variables inves-tigated in this study, tonnage and cushion pres-sure, showed a correlation with dimensionalmean shifts. No significant relationship could befound between material property variation andmean shift variation.

6. All stamping processes in this study operatedout of statistical control. Stamping processeshave inherent complexity making it difficult orimpossible in production to set up repeatedlywith a constant mean value on all panel dimen-

sions. For this reason, conventional X-bar and Rcharts are inappropriate for process controlbecause they would routinely indicate that theprocesses are out of control, despite the capa-bility to be assembled into acceptable bodies.Some manufacturers are better than others atminimizing mean shift variation, but all manu-facturers in this study are producing a signifi-cant percentage of parts with dimensions out-side of their assigned tolerances. The meaning-fulness of currently assigned tolerances tosheet metal part dimensions is suspect, partic-ularly for less rigid panels.

38

Page 46: Automotive Sheet Steel Stamping Process Variation

39

Appendix

Page 47: Automotive Sheet Steel Stamping Process Variation
Page 48: Automotive Sheet Steel Stamping Process Variation

Appendix A - Part Sketches by Company

Locating PinU/D & F/A

FrontPillar

CenterPillar Quarter

Inner

ClampsDetail Fixture

Pin U/D

ClampsDetail Fixture

Bodyside Outer

WheelhouseOuter

CenterPillar

QuarterInner

Locating PinU/D & F/A

FrontPillar

Figure 17. Part Sketches at Company A

Figure 18. Part Sketches at Company B

41

Page 49: Automotive Sheet Steel Stamping Process Variation

Bodyside Outer

CenterPillar

Not included:A-Pillar Upper and Lower ReinforcementB-Pillar Upper and Lower Reinforcement

Quarter Outer

ClampsDetail Fixture

Bodyside

CenterPillar

Windshield FrameReinforcement

Locating PinU/D & F/A

Front Pillar

Figure 19. Part Sketches at Company C

Figure 20. Part Sketches at Company D

Roof Rail

42

Page 50: Automotive Sheet Steel Stamping Process Variation

Bodyside InnerCenter PillarReinforcement

Quarter Outer

ClampsDetail Fixture

Bodyside Panel

Center PillarAssembly

Locating PinU/D & F/A

Pin U/D

Front PillarAssembly

Figure 21. Part Sketches at Company

Figure 22. Part Sketches at Company F

Roof Rail

Quarter Outer

ClampsDetail Fixture

Bodyside Outer

43

Page 51: Automotive Sheet Steel Stamping Process Variation

ClampsDetail Fixture

BodysideOuter

Center Pillar

Locating PinU/D & F/A

Front Pillar

Figure 23. Part Sketches at Company G

Quarter Inner

44

Page 52: Automotive Sheet Steel Stamping Process Variation

AK Steel Corporation

Bethlehem Steel Corporation

DaimlerChrysler Corporation

Dofasco Inc.

Ford Motor Company

General Motors Corporation

Ispat/Inland Inc.

LTV Steel Company

National Steel Corporation

Rouge Steel Company

Stelco Inc.

U.S. Steel Group, a Unit of USX Corporation

WCI Steel, Inc.

Weirton Steel Corporation

Auto/SteelPartnership

This publication was prepared by:

Body Systems Analysis Project TeamThe Auto/Steel Partnership Program

2000 Town Center, Suite 320Southfield, Michigan 48075-1123248.356.8511 faxhttp://www.a-sp.org

A/SP-9030-3 0100 2M PROGPrinted in U.S.A.

Automotive Sheet Steel

Stamping Process Variation

An analysis of stamping

process capability and

implications for design,

die tryout and process

control.

Auto/Steel Partnership

Page 53: Automotive Sheet Steel Stamping Process Variation
Page 54: Automotive Sheet Steel Stamping Process Variation

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