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NSF Engineering Research Center (ERC) forReconfigurable Manufacturing Systems (RMS)Reconfigurable Manufacturing Systems (RMS)
ERC – Big Three Quarterly Review MeetingERC Big Three Quarterly Review Meeting
March 16, 2009,
The University of Michigan, College of Engineering
Project review HistoryProj Project Reviewed
TA Proj# Project Title
j12/13/07 03/05/08 06/06/08 09/12/08 12/05/08 03/16/09 05/18/09
1Data Analysis and Causal Identification for Gear Noise Reduction in Transmission Systems
2Cyclic Waveform Signal Analysis for Monitoring and Control of Powertrain Manufacturing Systems
Iand Control of Powertrain Manufacturing Systems
3Throughput Analysis of Mfg Systems with Closed Loop MHS
4Computer Aided Simulation Model Verification, Testing and Optimization
1Hardware-in-the-Loop Simulation for Verification and Validation of Logic Control
2Manufacturing Network Time Synchronization Best Practices (NIST Funded)Reducing Unscheduled Downtime Through
II 3Reducing Unscheduled Downtime Through Automated Event-Based Control
4 Development, Application and Transfer of a Network ROI Cost Calculator (on hold after 12/07)
5 Wireless Network Analysis and Testing6 The Reconfigurable Factory Testbed (RFT)
III
1 Cylinder Bore Inspection
2 Pore Detection in Small Diameter Bores
3 In-line Vale Seat Inspection
Y. Koren Overview #2
III 3 In line Vale Seat Inspection
4 Thread Measurement
5 Camshaft / Crankshaft Polishing Testing
Meeting Agenda
2:00 Introduction
2:05 – 2:40 Reducing unscheduled downtime through automated event-based control James Moyne
C li f i l l i f it i d t l2:40 – 3:10 Cyclic waveform signal analysis for monitoring and control of powertrain manufacturing systems Judy Jin
3:10 – 3:30 Computer aided simulation model verification, testing and optimization Sam Yangp
3:30 – 3:40 Break
3:40 – 4:05 In-line valve seat inspection / PKM introduction Reuven Katz/Hagay B.
4:05 – 4:25 Thread Measurement Reuven Katz/Hongwei Z4:05 4:25 Thread Measurement Reuven Katz/Hongwei Z
4:25 – 4:35 New proposal: run-out measurement of crankshaft sprocket Reuven Katz
Discussions:
4:35 – 5:00
Discussions: • Acceleration of tech implementation and transfer • Third-party vendors involvement format• Wrap-up and next steps
All
5 00 Adj
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering
Y. Koren Overview #3
5:00 Adjourn
Engineering Research Center forReconfigurable Manufacturing Systemsg g y
Reducing Unscheduled Downtime Through Automated Event-based Control
Dr. James Moyne—UMProf. Dawn Tilbury –UM
Jeff Dobski—Global EngineStudent Lead: David Linz
Supporting Students: Garima Garg Edwin Teng Deepak SharmaSupporting Students: Garima Garg, Edwin Teng, Deepak Sharma
QRM, March 16th, 2009
Indicates New Result
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 1
The University of Michigan, Ann Arbor
Outline• Introduction
– The idea, objectives and approach
• Project History
C t F• Current Focus– Addressing changing conditions– Interim results
• Next Steps– Short termShort term– Longer term project planning– Summary and discussion
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 2
The Basic Idea:Reducing Costs Due to MaintenanceThe overall cost per part goes
Cost
The overall cost per part goes way up due to the higher per
unit cost of unscheduled downtime This is the
UnScheduledUnSched ledIdealized Cap
downtimeopportunity for fault
predictionThe benefit of lower scheduled downtime is nearly wiped out by the
Scheduled Downtime
UnScheduled Downtime
Scheduled
UnScheduled Downtime
Scheduled D ti
UnScheduled Downtime
Idealized Cap.(no downtime)
nearly wiped out by the unscheduled downtime (due
to longer times for diagnosis) and f
Production
Scheduled Downtime
Production
Downtime
Production
ProductivityLow due to conservative approach to maintenance
caused by high cost of
diagnosis), and…With fault prediction in place unscheduled downs are
reduced (turned into scheduled)
OptimizedPractice
AggressiveMaintenance Strategy
CurrentPractice
ProductionProduction y gunscheduled downsMore aggressive approach
to maintenance improves productivity slightly,
scheduled)
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 3
PracticeMaintenance StrategyPracticeproductivity slightly, however…
The Basic Idea:Common Approaches To Scheduling PM• Manufacturers Estimates: Usually fixed times listed in the manual
Problems: Tend to be overly conservative, do not reflect factory conditions
E ti ti MTTF• Estimating MTTF: Observing failures and adjusting time to failure estimates; support part-count maintenance as necessary
Problems: Does not account for variance in machine downtimes
• Reliability Studies: Conducting independent statistical studies to model reliability
Problems: Expensive, requires factory downtime to conduct testProblems: Expensive, requires factory downtime to conduct test
• Failure Prediction through Data Corelation – Accounts for Variance
O ti i ff ti– Optimizes effectiveness – Reduces Costs– Can be implemented with event data
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 4
Project Objectives and Approach• Short-Term: Help Global Engine identify gaps in plant-floor systems
– Better utilization of systems– Improve data quality and data usability of these systemImprove data quality and data usability of these system
• Mid-Term: Predict and reduce unscheduled downtime– Provide solutions for auto correlation of data sets
• Long-Term: Schedule preventive maintenance through ECA rule based control
• Continuous: Provide and help implement best practices in data• Continuous: Provide and help implement best practices in data management for improvement in maintenance management and downtime prediction
– Improving maintenance data quality– Maintenance pooling– Matching practice to specification– Adjust project focus as necessary to adopt to the impact of changing economic conditions
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 5
ECA Control SystemOptimize
Approach: Closing the Loop at Global Engine
Diagnostics Equipment/Tool Control
MaintenanceManagement
AutomaticShutdown
OptimizeMaintenanceScheduling
Shutdown
BENEFITSData Engineers
Operations with Top Fault Count
0
10
20
30
40
50
60
OP300_1 OP110_2 OP60_3 OP160_3 OP280_3
Operations
Dev
iatio
n fr
om A
vera
ge
Faul
tCou
nt
0
20
40
60
80
100
120
Cum
ulat
ive
Perc
enta
ge
Excel• Reduced
Unscheduled Downtime
• ReducedTODAY
StoresEngineers
MachineAdjustment
p
OPC
•••
Reduced Scrap
• Reduced MTTR
TODAY
Production Machines
PLCs
Production Machines
CNC’s Production Machines
PLCs
Production Machines
CNC’s
••• • Improved Productivity
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 6
Machines Machines Machines Machines
Outline• Introduction
– The idea, objectives and approach
• Project History
C t F• Current Focus– Addressing changing conditions– Interim results
• Next Steps– Short termShort term– Longer term project planning– Summary and discussion
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 7
Previous Deliverables• Look at main cost drivers of scrap, unscheduled tool changes and
unscheduled maintenance
• Top Ten anomalies software installed at Global Engine andTop Ten anomalies software installed at Global Engine and evaluated
– Recognizing interesting events in process data– Generate Excel report including Paretos
» User interface design per Global Engine specifications
Lack of good data quality hurt effectiveness somewhat» User interface design per Global Engine specifications
• Matlab analysis module for maintenance event correlation installed at Global Engine and evaluated
– Automated drill-down tool for maintenance investigationg– C++ code auto-generated from MATLAB source no MATLAB license required
• Best practices for improving maintenance management and data qualityq y
– Analysis of PM scheduling and reporting data quality– Comparing maintenance practices to documented maintenance requirements
• Student internships at Global Engine for Summers ’06, ‘07 and ‘08
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 8
Top 10 Pareto ChartO ti ith T F lt D ti
• Screen shot of viewing the top
Operations with Top Fault Durations
15000
20000
25000
iatio
n A
vera
ge
otal
at
ion
ec)
60
80
100
120
ulat
ive
enta
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viewing the top ten anomalies
– Faultiest Operations
0
5000
10000
P180_
8
P190_
2
P180_
4
P250_
1
P300_
1
Dev
ifr
om A To
Dur
a(s
e
0
20
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Cum
uPe
rce
OperationOperations
– Fault Count– Fault Total Duration
OP18
OP19
OP18
OP25
OP30
OperationsOperations with Top Fault Count
• Excel output– Fast learning ramp-
up– Ease of drill-down 30
40
50
60
iatio
n A
vera
ge
t Cou
nt
60
80
100
120
ulat
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enta
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– Ease of drill-down
0
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OP300 1 OP110 2 OP60 3 OP160 3 OP280 3
Dev
from
AFa
ult
0
20
40
Cum
uPe
rce
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 9
_ _ _ _ _
Operations
Previous Result: Strengthening Correlations with Normalized Overlay
A number of weaker Correlations can be grouped with overlays
After an overlay this operation demonstrates a gradual increaseAfter an overlay, this operation demonstrates a gradual increase in faults over the week leading up to the unscheduled Maintenance. This can be used as a basis for predicting and rescheduling downtime
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 10
rescheduling downtime.
Outline• Introduction
– The idea, objectives and approach
• Project History
C t F• Current Focus– Addressing changing conditions– Interim results
• Next Steps– Short termShort term– Longer term project planning– Summary and discussion
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 11
Current Focus• Failure Prediction
– Single fault type to maintenance
Leveraging improvements in data quality due to ongoing implementation of bestg yp
– All fault types to maintenance
Maintenance Under Low Production Conditions
implementation of best practice improvements
• Maintenance Under Low Production Conditions– Leveraging prediction information
• Reducing Redundant Maintenance– Best practices for matching unscheduled and scheduled
maintenance workmaintenance work– Leveraging knowledge of unscheduled maintenance events
in impacting scheduling maintenance
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 12
Failure Prediction Through Event Data:Single Fault Type to MaintenanceSeveral Operations show strong correlations within the time scope examined, there are relationships that can be observed:
Leveraging improved maintenance data qualityContinuous Reject Faults maintenance data quality through implementation of best practices
Leveraging improved UM-ERC capabilities for
BOLTFEEDER NOT FEEDING BOLTS @ 18:21BOLTFEEDER NOT NOSE PIECE WONT
ERC capabilities for analyzing per specific fault type, and for more automated analysis
BOLTFEEDER NOT FEEDING BOLTS @23:48 NOT FEEDING BOLTS @
21:01NOT FEEDING BOLTS@ 0:49 (Next Day)
BOLT FEEDER NOT WORKING @ 6:05“ “ @ 15:39
NOSE PIECE WONT CLAMP TOGETHER @ 12:23“ “ @ 23:46- Feed & Torque Station
@ 23:46( y)
It is not clear that these correlations will hold in the long term. However, if a relationship is confirmed, maintenance practices can be updated to reflect this knowledge
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 13
practices can be updated to reflect this knowledge
The data is now of sufficient quality to where these relationships can be confirmed our next step
Failure Prediction Through Event Data:All Fault Types to MaintenanceHigher correlations (once verified) means that we can use the prediction to improve impact of scheduled maintenance
160
180
200regression -7.545963e-001, mtype = AAA179608CPM type =grap all
Faults on System
BENEFITS• Reduces the probability of
unscheduled downtime
100
120
140
160 Incur cost due to lost units, slowing
production.
occurrences• Lower maintenance costs,
higher MTBF, lower MTTR, more predictable machine operation
40
60
80
100 predictable machine operation• Reduces the number of faults on
the system, increasing system performanceAlternative Schd.
Monthly Downtime
0 5 10 15 20 25 30 35 400
20
40
Unscheduled
• Allows maintenance scheduling to become adaptive
S
Monthly Downtime
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 14
Unscheduled Downtime
Schd. Monthly Downtime
Maintenance Under Lower Production Conditions:“Low Load Maintenance”
• In today’s world factories are not operating at capacity– Focus is on reducing cost
• However maintenance practices are often designed for at-capacity operation
– Maintenance needs are often a function of production (e.g., part count) rather than timethan time
– Maintenance practices are not adjusted when operating below capacity– Money is being lost due to overly aggressive static maintenance practices
Solution: Implement part count or event based maintenance• Solution: Implement part-count or event-based maintenance– Leverage part tracking and maintenance prediction– Leverage fact that maintenance system supports part-count based
maintenance triggersmaintenance triggers
• Issues– Part tracking insufficient to support reliable count-based triggering
Maintenance prediction needs to be verified with new data
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 15
– Maintenance prediction needs to be verified with new data
Maintenance Under Lower Production Conditions:“Low Load Maintenance”Example: Since the fault count on several operations track upward ahead of a maintenance operation as a result of part count, the prediction of the event can be used to trigger maintenance, resulting in longer times between maintenance in low load production conditionsp
High Fault Rate tracks High Fault Rate tracks System Load
BENEFITS• This alternative leverages existing information on the system
It d ’t i d t t th t t ki t• It doesn’t require an update to the part tracking system• The maintenance system can support event-based triggering
ISSUES• Need to verify causal relationships with newer higher quality data
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 16
y p g q y
Reducing Redundant Maintenance (1)In a typical time-based maintenance system, a sudden failure can trigger an unscheduled maintenance event
Schd. PMSchd. PM Failure! Schd. PM
time
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 17
Reducing Redundant Maintenance (2)If the unscheduled PM work is “matched” to the Scheduled PM, the PM schedule, if left unchanged is redundant and non-optimal
UnscheduledSchd. PMSchd. PM Schd. PM
Unscheduled PM
Redundant; mis-timed
time
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 18
Reducing Redundant Maintenance (3)The maintenance scheduled should be “reset”, resulting in fewer maintenance events, lower downtime, and lower maintenance costs
UnscheduledSchd. PMSchd. PM Schd. PM
Unscheduled PM
KEYS TO MAKING THIS WORKKEYS TO MAKING THIS WORK• Linkage between unscheduled maintenance events and triggers in DataStream• Matching Unscheduled to Scheduled Maintenance work orders
best practices
time
Datastream Counter Reset
p
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 19
Other Ongoing Efforts of Interest:Unified Data LayerEffort to integrate various data systems to increase the accuracy of regression analysis. An audit of all databases was performed to evaluate data quality and possibility of creating a unified data layer.
BENEFITS
ActivPlant
Global Engin Databases BENEFITS• Allows for a more
comprehensive visualisation and
d t di f
META
DataStream
understanding of factory data.
• Facilitates correlation analysis across many
i blxxxxx
xxxxxx
x
A
DATataSt ea
MPTS
variables• Allows “islands of
automation” to be focused on the same
xxxxxx
xxxxx
xxxxxx
xxxxx
xxxx xxx xxxx
EngineerCorrelation across multiple variables
TA
LA
Scrap Data
MPTS factory objectivesISSUES* Poor data quality
prevents unification of
AYER
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 20
prevents unification of data.
Other Ongoing Efforts of Interest:Improving Best Practices on Test Stands
Tear DownPass Engine Update the Book of Knowledge
1.) No Problems in the Engine (Engine Passed) (90%) of time2.) Minor Problems that can be fixed at cold stand (very rare) 3.) Major Problems, Engine needs to be Torn Down
) O ti 1 bl i t i th B k f k l d d
g
Test
a.) Option 1: problem exists in the Book of knowledge, and root cause can be identified. Result: Fix Operation Identified.b.) Option 2: problem's root cause cannot be identified. Engine is not immediately torn down due to lack of time and resourcesTest
Stand c.) Option 3: Engine is immediately torn down and operation may halt in order to determine root cause.
resources.
Operation One
Operation Two
Operation Three
Operation Four
KEYS TO IMPROVING BEST PRACTICES
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 21
• The main decision that needs to be made is whether a reject that has not been observed is worth examining the root causes of the problem
• The Largest Area of improvement is to determine a way for modeling the Likelihood of a reject seen occurring again
Outline• Introduction
– The idea, objectives and approach
• Project History
C t F• Current Focus– Addressing changing conditions– Interim results
• Next Steps– Short termShort term– Longer term project planning– Summary and discussion
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 22
Next Steps:Failure Prediction
• Project has shifted focus somewhat in light of the current economic conditions
– Focus on reducing cost NOW– Ideas for reducing cost in light of operation under capacity
• Data Quality best practices improvements are• Data Quality best practices improvements are starting to pay off
– We have evidence of correlations between faults and maintenance events in the latest datamaintenance events in the latest data
– We need to verify these correlations in new data coming in– Unfortunately older data (pre-best practices improvements) is
not that useful for verification– Unfortunately some of the newest data reflects operation much
under capacity» We have to decouple the capacity issue to verify correlation
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 23
Next Steps:Low Production Load Maintenance• Correlation, if verified will allow for implementation of
event-based maintenance– Newer data (reflecting newer best practices) should be of sufficient
quality to (1) verify correlations, and (2) determine if maintenance is a function of part count as opposed to (or in addition to) time
• Implementation of improved part tracking and linking to p p p g gmaintenance for part-count based maintenance scheduling is a longer term issue
• Longer term all three types of maintenance scheduling• Longer term, all three types of maintenance scheduling should be in place, based on prediction information (or lack thereof)
Time-based maintenance– Time-based maintenance– Part-count based maintenance– Event-based maintenance– Combinations
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 24
Next Steps:Reducing Redundant Maintenance
• Global Engine is aware of what needs to be done
• Fix is largely a combination of engineering and bestFix is largely a combination of engineering and best practices improvement at this point
– Linking of unscheduled corrective maintenance events into the preventative maintenance systempreventative maintenance system
– Aligning the work order descriptions of unscheduled maintenance operations with scheduled maintenance operations
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 25
Next Steps:ROI Calculations• Impact of current efforts should be easy to
convert into ROI numbersE d d b f i t it ti X t f– E.g., reduced number of maintenances per unit time X cost of maintenance event in terms of man hours and consumables
– We hope to use ROI data to drive additional improvements in i t ti d b t tiintegration and best practices
» Improved part tracking / counting» Aligning of unscheduled and scheduled downtime work order
descriptionsdescriptions» Resetting capability for maintenance schedules
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 26
Next Steps:Other Efforts
• Data consolidation / meta layer
• Improve best practices on test stands; decision process with rejects; analysis of continuous data
E l i ti MPTS d t i t d l f• Explore incorporating MPTS data into model for Unscheduled Downtime Prediction
C ti t i t i l t ti d fi t f• Continue to impact implementation and refinement of best practice improvements
– Data quality improvements to support prediction, consolidation and ROIData quality improvements to support prediction, consolidation and ROI goals
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 27
Summary
• Implementation of best practices for improved data quality are starting to pay off
– We are seeing potential correlations between faults and maintenance– We are seeing potential correlations between faults and maintenance events
– Signal could be strong enough for prediction
• Current focus areas reflect economic environment
– Fault prediction maintenance schedulingau t p ed ct o a te a ce sc edu g– Low production load maintenance scheduling improvements– Reducing redundant maintenance
• Next steps focus on verifying analysis with new data and providing justification for best practice improvement investment through ROI analysis
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 28
improvement investment through ROI analysis
Discussion
• Need to determine the type and level of longer term support for the project
– No Summer internship in 2009– No Summer internship in 2009– Will likely have close interaction with Global Engine via regular visits
• Need to determine if there is reusability– Elsewhere in Chrysler– Other ERC members
• If there is a desire to close the project 4 – 6• If there is a desire to close the project, 4 – 6 months additional to tidy up deliverables would be ideal
– There is still research to be done– Prioritization with other projects
• Questions?NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 29
Questions?
Backup SlidesBackup Slides
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 30
Examples of Data Quality Issues Encountered• System collects only a subset of the data generated from the PLCs.
There is often not enough information to identify strong correlations to support control.
• Historical Data is limited, making larger trends and behavior difficultHistorical Data is limited, making larger trends and behavior difficult to identify.
• Maintenance records have missing data.• Insufficient standardisation of data, manually entered data , y
unsuitable for computerised analysis.• Scrap code definition process results in unbounded growth of codes
nearly useless for correlation analysisAd-hoc scrap code creation
Scra
p s
Num
ber o
f SC
odes
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 31
Time
Samples ofRecommendations for Improving Data Quality• Modular and Hierarchical “Reason Code” Scheme
– Incorporate an ID system for maintenance to help record keeping and reduce redundancy
– E.g., Maintenance and scrap databases
• System Wide Data Unification– Create a factory meta-data layer for access analysis and drill-downCreate a factory meta data layer for access, analysis and drill down– Create a unified labeling system so that a part can be tracked
through the system.
• Extended Access to Historical Data• Extended Access to Historical Data– Build in a mechanism for access to large vectors of archived data.
• “Deep” Analysis Of Key Operations– Select a single operation within the system and analyse its
behaviour over a long historical period to understand fault alarms and downtime behavior
– Use this process to better identify underlying data quality issues
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 32
Use this process to better identify underlying data quality issues
ROI of Scaling Maintenance to Factory Load While there may be additional costs to implement a scaling system; the reduction of costs due to unnecessarily performed downtime indicate a high cost benefit ratio.
BENEFITSR d d t d t• Reduced costs due to un-necessary maintenances on the system.
INVESTMENTS• A system by which to
track parts.OR • Increased Production
time.• The ability to cope
variable production
--OR—• A mechanism for
scheduling downtimes w.r.t fault data. p
situations.
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 33
Dataflow to Assist Analysis
MS Access (Manual)
ScrapGEMA
Int. Maint. HistoricalDatabase
ActivPlant PPS/PDCA Predict MS Access (Manual)
Machine
Operations with Top Fault Count
0
10
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30
40
50
60
evia
tion
from
ve
rage
Fau
lt C
ount
0
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60
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120
Cum
ulat
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Perc
enta
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Graphs
ActivPlant PPS/PDCA
Number
Unscheduled Downtime
ActivPlantJavaScript Excel
Faults
M i t
OP300_1OP110_2
OP60_3OP160_3OP280_3
Operations
De A C P
Operations with Top Fault Count
50
60
ge
t 100
120
e e
Graphs, Correlations
Number, duration of faults
DataStream MATLAB (C++)
JavaScriptAnalysis
Maint.CM, PM 0
10
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OP300_1 OP110_2 OP60_3 OP160_3 OP280_3
Operations
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gFa
ultC
oun
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80
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Maint, faults based on d ti
Graphs, Correlations
Excel (Manual)MPTS
Plan: Put machine faults together with machine rejects; incorporate information from Unschd. Tool Change and Scrap data if possible. (see
MachineReject
Unsch.Tool Change.
Excel (Manual)
duration
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 34
MPTS dotted line)Excel (Manual)
UML DATA LAYER (CURRENT)
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 35
NSF Engineering Research Center (ERC) forReconfigurable Manufacturing Systems (RMS)
C li W f Si l A l i f M it i
R h T
Cyclic Waveform Signal Analysis for Monitoring & Diagnosis of Powertrain Manufacturing Systems
Research Team:ERC/UM: Judy Jin, Kamran Paynabar, Yong Lei, Qiang Li, GM: John Agapiou (R&D);
Ed S ll & St N (GMPT Li i )Ed Sponseller & Steven Norman (GMPT- Livonia);Thomas Gustafson & Phillip Steinacker (GMPT-Pontiac)
Chrysler: James Wang, Eugene Kuo, Mark Skelly, John Gartner
March 16, 2009Presenter: Judy Jin
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 1
Outline
• Project Overview & Process Background
• Accomplishments & Tech Transfer Plan at GM• Accomplishments & Tech Transfer Plan at GM
• New investigations at ChryslerComparison of data collection between Chrysler and GM– Comparison of data collection between Chrysler and GM
– Analysis of available original waveform signals at Chrysler
• New project proposed by GM• New project proposed by GM
• Milestone & Future Works
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 2
Project OverviewProblem
Goal
Problem• Cyclic waveform signals are widely used for online monitoring of powertrain manufacturing process.• The trial and errors approach for features extraction & monitoring limits may not always be effective.
• To develop systematic waveform signal analysis methods to improve online monitoring systems for powertrain manufacturing processes.
Deliverables and benefitsD l i i l l f b d l f li f i i l tDevelop a generic signal analyzer for a broad class of cyclic waveform sensing signals to
• automatically set up monitoring limits to improve first time quality and reduce ramp up time for new production lines;
• effectively extract monitoring features from online sensing signals to reduce both false rejects and miss detection for reducing mfg cost;
Main tasksW k ith i t GM (&Ch l i thi t ) t ll t d ti /DOE d t f th
and miss detection for reducing mfg cost;• continuously learn and enhance diagnostic capability to quickly identify the root causes for
reducing defects & downtime.
• Work with engineers at GM (&Chrysler in this quarter) to collect production/DOE data from the selected process;
• Develop data analysis algorithms for data preprocessing/signal alignment/signal segmentation /online monitoring charts to characterize/monitor process operation states;
• Characterize process fault patterns to enhance diagnostic capability;
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 3
p p g p y;• Software development, plant testing, and validation.
Process Background
Valve Seat Pressing Machine Sciemetric
SystemProblem: A high false reject rate is a top concern.
Sensor data Goal: Improve production throughput by effectively using online sensor
i imonitoring systems.
Recorded manual inspection 28%
True Detection65%
2000
3000
4000
ad
Peak Force
GapUnrecorded
inspection72%
Reduced to5% by ERC
0
1000
2000
Loa
Work
Force
Depth
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 4
-0.25 -0.2 -0.15 -0.1 -0.05 0Distance
p
Overview of Proposed New Monitoring Methods High dimensional Cyclic waveform
signal analyzerLow dimensional
physical interpreted features
High dimensional non-stationary sensing signals
Monitoringcontrol limits
SL
Root cause: misaligned signals.
SLfalse reject
3000
4000
5000
6000 Force • Alignment algorithms• Segmentation algorithms• Feature extraction algorithms• Online monitoring limits
-0.3 -0.25 -0.2 -0.15 -0.1 -0.05 0 0.05-0.35-1000
0
1000
2000
LVDT
• Online monitoring limits• Fault classification
M lti l l t l i Sciemetric depthNew aligned depth by UMMultiscale wavelet analysis
yk1h
ykd,1
ykid ,
...0jh
l
ykjc ,10−
ykjd ,0
ykjc ,0
1lykc ,1 ...
ykic ,1−
ykic,
,ih
il
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 5
0jlkj ,0
Accomplishments & Tech Transfer Plan at GM
Major Accomplishments: ERC-team has proposed new algorithms for false reject reduction, the validations show:• UM algorithms can help reduce false rejects from the current 35% to 5%; • UM algorithms have the potential benefit to reduce miss detection of bad
products by setting more effective specification limits.
Current Tech Transfer Status:• GM is interested in implementing new algorithms and initiated efforts for the• GM is interested in implementing new algorithms and initiated efforts for the
ERC/UM team to work with the Sciemetric Company.• Technology transfer agreement was made between ERC, GM and Sciemetric
Company.p y• In this quarter, UM has provided the program codes to Sciemetric Company,
which will be implemented into GM monitoring system for further validation.
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 6
New investigations at gChrysler in this quarter
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 7
Comparison of Data Collection Schemes Between Chrysler and GMBetween Chrysler and GM
GMPT at Livonia Chrysler at Mac1
Accepted parts
Rejected parts
Accepted parts
Rejected parts
Original sensing signalsEvery 100 parts
All parts Last 25 parts Last 25 parts
Scimetric features data Not saved All parts All parts All partsScimetric features data Not saved All parts All parts All parts
Two analyses have been done based on available original waveform signals:y g g• For the rejected samples having regular signal profiles (8 samples), are they false
rejects?• For the rejected samples having irregular signal profiles (17samples), what are the
root causes?
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 8
Analysis 1: Investigate whether Sciemetric rejected samples with
regular signal profiles are false rejects or not (8samples).
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 9
Rejections Based on Old Sciemetric Depth
UL=0.5
(in)
Parts are rejected by the Sciemetric depth feature. (suspect of false reject)
LL=-0.5
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 1010
Signal Comparison between Rejected and Nonrejected Parts
Specification Limits
8000
10000 Specification Limits
Blue: 25 samples of accepted parts by Sciemetric
Red: 8 samples rejected by Sciemetric
6000
bs)
Red: 8 samples rejected by Sciemetric
(suspected false rejects)
4000
Forc
e (lb
Comment:
Misaligned signals may cause false rejects
0
2000g g y j
based on Sciemetric “depth” feature.
-35 -30 -25 -20 -15 -10 -5 0 5-2000
0
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 11
LVDT (in)
New Feature “Aligned Depth” after Signal Alignment
Algorithm of calculating aligned depth:Algorithm of calculating aligned depth:1. Use wavelet analysis (Harr transformation) to find contact point.
Contact point: The point where the press tool actually contacts the part leading to the change point of force signals.g g p g
2. Calculate new feature “aligned depth”. The difference between the contact point and the maximum LVDT value, which reflects the actual moving range.
---- Out-of-control---- In-control
3000
3500
4000
4500
3500
4000
4500
1500
2000
2500
1500
2000
2500
3000
Aligned by contact point
-500
0
500
1000
0 1 0 05 0 0 05 0 1 0 15 0 2-500
0
500
1000
Contact point
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 12
Misaligned Signals-0.25 -0.2 -0.15 -0.1 -0.05 0 0.05 0.1 -0.1 -0.05 0 0.05 0.1 0.15 0.2
Aligned Depth
Signals After Alignment
8000
10000
Blue: 25 samples of accepted parts by
6000
(lbs)
Sciemetric
Red: 8 samples rejected by Sciemetric
(suspected false reject rate based on 254000
Forc
e (suspected false reject rate based on 25
samples=8/25=32%)
Comment:
0
2000 Comment:
Aligned depths are
within the limits.
-25 -20 -15 -10 -5 0 5 10-2000
LVDT (in)
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 13
LVDT (in)
Monitoring Based on New Aligned Depth
UL=6.93
(in)
LL=5.93
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 1414
Request of New Data Collection for Further Validation at ChryslerFurther Validation at Chrysler
1. Record the original sensing signals for every 100 parts to verify
th “d th” f tthe new “depth” feature;
2. Record quality inspection data to verify rejections.
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 1515
Analysis 2:Cl if I l Si l P fil f R j d PClassify Irregular Signal Profiles of Rejected Parts
(17 samples)
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 16
Irregular Shape Signals at Chrysler: Missing seats
8000
10000
10
-5
4000
6000
8000
e (lb
s)
-20
-15
-10
e (lb
s)(in
)
0
2000
4000
Forc
e
-30
-25
Forc
eLV
DT(
0 200 400 600 800 1000 1200 1400-2000
0
Time (ms)
0 500 1000 1500-35
Time (ms)
6000
8000
10000
2000
4000
Forc
e (lb
s)
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 17-35 -30 -25 -20 -15 -10 -5
-2000
0
LVDT (in)
100
120
28
-27.5Irregular Shape Signals at Chrysler: Interrupted operations
40
60
80
e (lb
s)-28.5
-28
DT
(in)
0
20
40
Forc
e
-29.5
-29LVD
1200 200 400 600 800 1000 1200
-40
-20
Time (ms)
0 200 400 600 800 1000 1200-30
Time (ms)
60
80
100
bs)
0
20
40
Forc
e (lb
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 18-30 -29.5 -29 -28.5 -28 -27.5
-40
-20
LVDT (in)
Mapping Table Developed based on GM Data___ Example of classified irregular profiles at GM
Fault type LVDT Force
N lHighly oscillatedLVDT
(spring problem)High LVDT
NormalHighly oscillated LVDT
Force (cable problem)
& LVDTOscillated LVDT(hi h i )
Normal shapeNo correct readingHighly oscillated
LVDT& LVDT
(spring problem)(high variance)
p
Missing Part NormalNo local forceHigh value of peak Normal
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 19
Improved Diagnostic Decision for Monitoring System
In‐comingSensor Signal Improvement by UM
Provide fault report &correction action Yes
No
• To Add a detection& classification module.
Detect & classify Irregular signal profiles?
Calculate monitoring features
• Developed better monitoring features (e.g. aligned depth)
Out of specification limits (SL)?
No
Confirm fault detection
Yes
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 20
New Project Proposed by GM__ Improve Leaking Test
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 21
New project at GM: Improve ATC Leaking Test
The Whole Leak Testing System
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 22
New Project Activities with GMSite visit:
Identify new projects:
•UM and GM teams visited Advanced Test Concepts (ATC) Inc. in Indianapolis on Feb 9&10;
Identify new projects:•Project 1: Signature analysis and self-learning of fault patterns for abnormal detection and root cause identification .
•Project 2: Verification of the adaptive test strategy to reduce the leak test cycle time and improve the production throughput.
•Project 3: Development of the standardized procedures for self-calibrating test•Project 3: Development of the standardized procedures for self-calibrating test tools at the tool setup stage for reducing the setting up time and engineers’ trial and error calibration efforts.
Next step•Work with the GM team to identify a plant, which can be used for the data collection and method/tool development in the next step.
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 23
Milestones and Future Plans• Defined project scope and candidate manufacturing processes.
Last Yearp j p g p
• Collected production data at GMPT plants at Pontiac and Livonia.• Understand current practice and the GM need.• Conducted experimental tests and analyzed DOE data. • Developed signal alignment algorithms using Wavelets.
• Found the root cause of high false rejects of Sciemtetric system.• Developed new algorithms for reducing false rejects.• Prove the concept of profile feature extraction for gap detection.
June 2008
p g g g g
• Collect more data for further validation of new features and signal alignment algorithms.
• Integrating our proposed alignment algorithm with Scimetric software.Dec 2008
W k ith S i t i f ft i l t ti
• Propose an algorithm for determining specification limits.• Analyze irregular fault patterns to enhance diagnostic capability for
detecting and classifying sensor problems and missing part.
• Work with Sciemetric company for software implementation.• Work with Chrysler for algorithms validation and implementation• Identify the new projects for improving leaking test at GM
• Continue validation analysis at Chrysler.
Next Quarter
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 24
Continue validation analysis at Chrysler.• Work with GM for the new projects of improving leaking tests.Next year
Thank you!Thank you!
Q & AQ & A
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 25
3. Analyzing Sciemetric feature data3. Analyzing Sciemetric feature data corresponding to accepted parts
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 26
00.20.4
th
Features box‐plot over timeDepth
1 2 3 4 5 6 8 9 10 11 12 15 16 17 18 19 29 30 36 37 38 39 40 43 44 45 46 47 49 51 52 53 56 57 58 59 63 64 65 66 67 70 71 72 77 78 79 80 81 84 85 86 87 88
-0.4-0.2
0
Dep
t
Date index
12000Work
1 2 3 4 6 8 9 10 11 12 1 16 1 18 19 29 30 36 3 38 39 40 43 44 4 46 4 49 1 2 3 6 8 9 63 64 6 66 6 0 1 2 8 9 80 81 84 8 86 8 88
400060008000
1000012000
1 2 3 4 5 6 8 9 10 11 12 15 16 17 18 19 29 30 36 37 38 39 40 43 44 45 46 47 49 51 52 53 56 57 58 59 63 64 65 66 67 70 71 72 77 78 79 80 81 84 85 86 87 88Date index
8000
9000
10000Peak
1 2 3 4 5 6 8 9 10 11 12 15 16 17 18 19 29 30 36 37 38 39 40 43 44 45 46 47 49 51 52 53 56 57 58 59 63 64 65 66 67 70 71 72 77 78 79 80 81 84 85 86 87 88
7000
Date index
3000
Force
1 2 3 4 5 6 8 9 10 11 12 15 16 17 18 19 29 30 36 37 38 39 40 43 44 45 46 47 49 51 52 53 56 57 58 59 63 64 65 66 67 70 71 72 77 78 79 80 81 84 85 86 87 88
1000
2000
D t i d
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 27
Date indexSep 08 Oct 08 Nov 08 Dec 08
Date index
Depth box‐plot over time (good parts)
0.3
0.4
Sep 08
0.1
0.2Sep 08
h
-0.1
0
Dep
th
Oct 08 Nov 08 Dec 08
Dep
th
-0.3
-0.2• An obvious shift in depth from Oct 08 • Possible reason:
• Change in cylinder head raw part
1 2 3 4 5 6 8 9 10 11 12 15 16 17 18 19 29 30 36 37 38 39 40 43 44 45 46 47 49 51 52 53 56 57 58 59 63 64 65 66 67 70 71 72 77 78 79 80 81 84 85 86 87 88
-0.5
-0.4• Change in cylinder head raw part• Resetting the production process
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 28
1 2 3 4 5 6 8 9 10 11 12 15 16 17 18 19 29 30 36 37 38 39 40 43 44 45 46 47 49 51 52 53 56 57 58 59 63 64 65 66 67 70 71 72 77 78 79 80 81 84 85 86 87 88Date indexDate index
Features Scatter Matrix (good parts)
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 29
Features Scatter Matrix (Oct to Dec)
Low peak andLow peak and low depth
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 30
Low Peak and high force
Projected features (Oct to Dec 08)
All data correspond to good parts
(based on Sciemetric report)
• Although all parts are good, they can beg p g yclustered in two classes by using features.
• To understand the reasons further study
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 31
is needed.
8000
10000
6000
Pink: part # 2 which is close to upper limits
Blue: other accepted parts
bs)
4000
Forc
e(lb
0
2000
1600
1800
-7 -6 -5 -4 -3 -2 -1 0-2000
LVDT(in)1000
1200
1400
LVDT(in)
400
600
800
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 32
-7 -6.8 -6.6 -6.4 -6.2 -6 -5.8 -5.6 -5.4 -5.2 -5-200
0
200
8000
6000
7000
8000
5000
6000
s)
Black: rejected parts close to the lower limit
Red: other rejected parts
3000
4000
Forc
e(lb
s
1000
2000
7 6 5 4 3 2 1 0-1000
0
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 33
-7 -6 -5 -4 -3 -2 -1 0LVDT(in)
10000
Aligned Signals
8000
6000
)
Blue: accepted parts
Black: rejected parts close to the lower limit
Red: other rejected parts4000
Forc
e (lb
s Red: other rejected parts
0
2000
1 0 1 2 3 4 5 6 7 8-2000
0
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 34
-1 0 1 2 3 4 5 6 7 8LVDT (in)
Detecting and Classifying Sensor Failures---- Proposed Mapping Algorithm by ERC/UM
Fault type LVDT ForceType I‐LVDT
(spring problem)Oscillated LVDT(high variance)
Normal(spring problem) (high variance)
Type II‐LVDTStep shape
(highly positive slope)Normal
Type III‐LVDTSpike shape
(highly positive & negative slope)
Normal
ith “ l f ”Type IV‐force
(cable problem)Normal
either “very low force” or over “peak force”
No correctType V‐force&LVDT
No correct reading/No slope
No correct reading/Oscillated force
(high variance)
No correct
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 35
Type VI‐missing Seat High LVDT reading reading/Oscillated force (high variance)
Signature OutputsQuick St bilit T tQuick Fill+Fill Stability Test
Master Part +Part +
EC
Master Part
Select limit to be used for leaking engines.
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 36
Identify Root Cause of High False Reject Rate6000
5000Blue: classified as a good part by Sciemetric
Red: false rejected as a bad part by Sciemetric
Force SL
3000
4000 Both signals seem to be good parts.
1000
2000 Root cause: misalignment on signals.
0
1000
-0.35 -0.3 -0.25 -0.2 -0.15 -0.1 -0.05 0 0.05-1000
Depth DepthSciemetric “depth” is based on LVDT absolute value, which is not atr e reflection of the act al mo ing range
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 37
p ptrue reflection of the actual moving range
NSF Engineering Research Center forReconfigurable Manufacturing SystemsReconfigurable Manufacturing Systems
Statistical Output Analysis of Steady-State p y ySimulation Models In SimuVeri Software
Sam Yang, Wencai Wang and Jack Hu (UM)Sam Yang, Wencai Wang and Jack Hu (UM) Susan Ostrowski and Annette Januszczak (Ford)
March 16, 2009
The University of Michigan, Ann Arbor
Outline
• Project Overviewj
• SimuVeri System Architecture
• Introduction to Statistical Output Analysis
• Application Case• Application Case
• What’s Next
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 2
Project Overview
• Model verification and experimenting
Background: The project arises from an earlier Ford-ERC project at CEP
• The manufacturing processes and models are very complex;Model verification and experimenting
are time consuming; • Models are not completely tested on
system level before experimenting;• Resulting in exaggerated throughput
and poor model applicability.Goals:
• To develop Computer Aided Testing (CAT) tools for automatic error checks;• To provide user friendly software• To provide user friendly software
platform supporting various validation, experimentation and optimization
techniques.
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 3
tec ques
Architecture of Application Software
Users select testing strategies
Engineers d l d l
Software executes tasks
& outputsdevelop model & outputs Results
WITNESS runs each scenario
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 4
each scenario
Introduction of Statistical Output Analysis
• Random inputs/parameters in the simulation models result in random observation outputs (performanceresult in random observation outputs (performance measure estimates) with some distribution.
• Questions about experimenting simulation models.Questions about experimenting simulation models.– How do we start the simulation (warm-up)?– How long should the model be run (how much simulated time
before stopping the run)?before stopping the run)?– How many samples of the performance measures should be
collected (how many replications)?H h ld th t t b l d?– How should the output be analyzed?
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 5
Steady State Simulations
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 6
Steady State Simulations
• The system has reached “steady state” where the performance is independent of the initial conditionsperformance is independent of the initial conditions.
• Examples– Production line simulations – the line starts where it left off at the
end of prior shifts.– Emergency rooms.Emergency rooms.– Airplane scheduling at airport.
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 7
Steady State and Running Time
• How to start the simulation?– Typically a warm-up period is used to minimize any impact of
initial conditions.– How long should the warm-up period be?How long should the warm up period be?– How long to run the simulation?
• Correlation Analysis
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 8
The Number of Replications
• Simulation models are used for experimentation.p– One simulation replication → a single sample (realization) of
each system performance measure.
n independent replications n independent samples from the– n independent replications → n independent samples from the same distribution.
• Confidence Interval Analysis
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 9
The Number of Replications
• Consider a single performance measure Let Xi be theConsider a single performance measure. Let Xi be the random variable that represents the value of the performance measure for the ith simulation replication.– xi = outcome/realization of Xi from the ith simulation replication.
• Since the Xi are independent and identically distributed d i bl th f b h t i drandom variables the performance can be characterized
using the “typical” confidence interval.
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 10
Analysis of Output (1)
• The approximate confidence interval%100*)1(• The approximate confidence interval %100*)1( α−
snszx *2/1 α−±
• Central limit Theorem: regardless of the distribution of h X ill b i t l di t ib t d)(Xeach Xi , will be approximately distributed as a
normal random variable when n ∞)(nX
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 11
Analysis of Output (2)
• The approximate confidence interval%100*)1(• The approximate confidence interval %100*)1( α−
snstx n *2/1,1 α−−±
• Assumes the sample average is from a normal di t ib tidistribution.
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 12
The Number of Replications
• How to estimate number of independent replications required for a• How to estimate number of independent replications required for a desired precision.
• The half-width h of this confidence interval is
*2/1,1 nsth n α−−=
level). (precision desired afor 2
22
2/1,1 hhstn n α−−=⇒
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 13
The Number of Replications
• Substitute• Substitute
2/1,12/1 for tz n αα −−−
2
22
2/1 *hszn α−≈⇒
• Use this formula to approximate the number of replications needed to get a desired half-width (precision) for someneeded to get a desired half width (precision) for some performance measure.
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 14
An Application Case
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 15
An Application Case
Time Observation
1.484055403 1.484055403
1.931330511 0.200569173
3.498247812 0.416304048
6.424550926 2.639221143
24.20430586 6.142266534
33.38210516 5.722407758
33.78572903 5.420538552
46.44300943 2.564226808
47 72927941 0 69581033447.72927941 0.695810334
60.97070454 12.17619063
80.76142078 30.96875537
AvgStddev
8.1870225.428285
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 16
An Application Case
2/1,12/1 for tz n αα −−−
2
22
2/1)A(*
hvgTISszn α−≈⇒
96.1025.01 =−z
4535.43.596.1 2
22 =≈n
Avg. 8.187022Stdev 5.428285
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 17
What’s NextLITERATURE REVIEW AND PROJECT DEFINITION
• Project needs and direction• Verification and validation approaches
WITNESS COMMUNICATION PROTOCOL• Witness file parser
• Witness file generator• Interfaces
VERIFICATION MODULE• Code the module
• Specific errors to be detected• Verification strategies
• Code the testing strategies
VALIDATION MODULE• Validation needs
• Validation strategies• Code strategies
OPTIMIZATION MODULE• Optimization needs
• Explore optimization methods• Code and test optimization
methods
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 18
Completed In Progress Future Work
Engineering Research Center for
Reconfigurable Manufacturing Systems
In-line Inspection of Engine Valve Seats
Dr Reuven Katz and Sankalp ArraboluDr. Reuven Katz and Sankalp Arrabolu
March,13th, 2009March,13 , 2009
The University of Michigan, College of Engineering
Seat angle
In-line inspection of engine valve seats
Gage Ф
Seat angle
Seat length
Valve guide
• Deck seat (±0 1o) throat anglesDeck, seat (±0.1 ), throat angles• Seat length• Seat roundness at gage• Seat runout at gage wrt valve guide
2NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering
Project Goal
• In-line measurement of valve seat geometry (cycle time ~ 45 seconds)
• Rapid and accurate non-contact measurement
• Measurement of seat angles and seat length
• Preliminary repeatability test
• Evaluate in-line application feasibility
• Comment: All the study was done without having a defined specification of the problem
3NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering
Approach 1: Single Cross Section
4NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering
Approach 2: Least Squares Cone Fit w/ Five Cross Sections
Seat angleSeat angle
ERC Results CMM ResultsRun 1 Run 2 Run 3 Run1 Run2 Run3
60 segment angle 60.53 59.39 59.99
Seat Angle 45 18 45 18 44 98 45 15 45 34 45 18Seat Angle 45.18 45.18 44.98 45.15 45.34 45.1830 segment angle 30.28 30.33 30.27Seat Length 1.7137 1.6979 1.6991 1.729 1.772 1.738
5NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering
Valve seats R&R test results
Results of the repeatability testperformed for 50 measurements across 5 cross‐sections
Seat Angle (degree)
Deck Angle (degree)
Throat Angle (degree)
Roundness (mm)
Seat Length (mm)
Gage Depth (mm)
Average 45.07468 60.40652 30.34172 0.02988506 1.747624 12.62412
Standard Deviation
0.017752769 0.043423985 0.017123847 0.002473562 0.012789657 0.002429748
6NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering
Accomplishments and Next Steps
Accomplishments:
• Angle measurement to within ± 1º degrees achieved using both happroaches
• Seat length measurement to within 0.2 mm
• Two axis demonstrator designed and built by ME450 studentsTwo axis demonstrator designed and built by ME450 students.
• Complete statistical analysis of cone-fits for improving accuracy.
• Design repeatability set-up and testing
Next Steps:
• Evaluate the implementation feasibility i e increase measurement• Evaluate the implementation feasibility i.e. increase measurement speed, optimize data collecting path
• Test serial robot and a evaluate the use of a PKM
7NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering
Operational Data of the Current Measuring System
• Optimet Conoprobe Laser Scan Frequency : 3000 KHz
• The Motion Stage Forward Motion Maximum Speed Possible : 5000 mm/min
• Current System (presently not optimized for time)y (p y p )Forward Speed used : 100mm/minBackward Speed used : 1000 mm/minStop time between scans : 1 secp
8NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering
Methods for Time Optimization Methods for Time Optimization
with above Specificationswith above Specifications
9NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering
Current Method Time Taken = 193 sec Time Taken= 176 sec
Time Taken = 88sec
Challenges
To find the minimum time : we need to increase the d f thspeed of the scan.
Increasing speed reduces the number of data points capturedcaptured.
The Speed Vs Data Capture is an OPEN ISSUE.
Need to decide which parameter to compromise on forNeed to decide which parameter to compromise on, for the best time optimized performance.
11NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering
Engineering Research Center for
Reconfigurable Manufacturing Systems
Parallel Kinematic Mechanism (PKM) for P i L ti f O ti l SPrecise Location of Optical Sensors
Dr. Hagay BambergerDr. Reuven Katz
Date: 3/16/2009
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering
The Goal & The MethodThe Goal & The Method
Develop & build a PKM demonstrator, which will precisely locateoptical sensors like camera or laseroptical sensors, like camera or laser
Use PKM for Valve Seat measurements as well as for SmallBores inspection projectsp p j
The suggested PKM possesses 4 degrees of freedom that arerequired for locating precisely optical sensors, within a desiredworkspace
The advantages of a PKM:A• Accuracy
• High rigidity• Large payload capacity• Fast dynamic response
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering
• Fast dynamic response
Description of the Suggested PKMDescription of the Suggested PKM
The mechanism consists of:M i l tf bl f 4 d f f d• Moving platform capable of 4 degrees of freedom:
two translations and two rotations• 4 linear motors on the base
Sight PipeMotion stageLaser
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering
Project LayoutProject Layout
Analysis & Synthesis:Ki ti D i W k Si l iti• Kinematics, Dynamics, Workspace, Singularities,
Structure, Joints …Detailed mechanical designDetailed control designBuilding and calibratingTests:Tests:• Accuracy, Repeatability, …
Required resources:Required resources:• CAD designer• Control expert
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering
• Budget of ~$30K for hardware
Estimated Timetable for PKM projectEstimated Timetable for PKM project
Starting: 4/1/2009
Mechanical design review: 5/2009
Control design review: 7/2009
Component purchasing &Component purchasing &manufacturing: 9/2009
Working prototype: 11/2009Working prototype: 11/2009
Tests: 12/2009
NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering
Engineering Research Center for
Reconfigurable Manufacturing Systems
Internal Thread MeasurementInternal Thread Measurement
Dr. Reuven Katz, Dr. Hongwei Zhang and Dr. En Hongg g g
Mar. 16, 2009
The University of Michigan, College of Engineering
Project Overview
Goals:Goals:• Develop methodologies for the inspection of geometrical features of
internal threads in machined automotive parts.
• The two methods to be presented enable in-process internal thread q alit erification sing optical sensors
Deliverables and benefits
quality verification using optical sensors.
• The approaches allow to extract thread pitch, major and minor diameter, flank angle and even the starting point of the thread with respect to a reference location on the perimeterreference location on the perimeter.
Main tasks• Laser scan measurement using Optimet sensorLaser scan measurement using Optimet sensor
• Optical inspection using a CCD camera with sightpipe
• R&R test to be done partially
2NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering
Methodology using a laser sensor and set-up
Principle
Rotary
Internal Thread
Principle
Z axisProbe
Z
Rotary Motion Mirror
OpticalSensor
X
Y Motorized
periscopeMotion Stages
X axis
Y axis
Rotary Stage
Motion StagesRotary Motion
Periscope
Sensor
Method:
Measuring internal threads using a Laser Range Finder (Optimet Sensor) Sensor
Stepper Motor
45˚ Mirror inside
integrated with a motorized periscope designed at ERC.
3NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering
Measurements results of M12X1.25 internal thread
Minor Diameter --D1 Pitch Diameter --D2 Major Diameter --DMin Max Min Max Tol Min Max
STANDARD: ANSI/ASME B1.13M-1983 (R1995)M12 X 1.25 Unit: mm
Measured Parameters
Min. Max. Min. Max. Tol. Min. Max.10.647 10.912 11.188 11.368 0.18 12 12.360
0˚ 180˚ 90˚ 270˚ AveragePitch (mm) 1.248 1.251 1.252 1.246 1.249
Height (mm) 0.639 0.696 0.539 0.615 0.622Major Dia. (mm) 12.014 12.127 12.071Minor Dia (mm) 10 667 10 871 10 769 Δ Pitch
Major D
ia.M
inor Dia.
Minor Dia. (mm) 10.667 10.871 10.769
Conclusion 1:
Δ
Compared to the standard data, all the parameters we get are
within acceptable limits for the designated thread type. axial cross section
4NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering
Measurements results of starting location of a thread
Method:
• Measuring the axial distance between the edge of the threadedMeasuring the axial distance between the edge of the threaded
bore and the center point of the bottom of the first thread tooth.
• Four measurements were taken at four different angles. The area
Area ofThread Started
90˚ Δ=2.288
of thread started is determined by comparing these four values.
• Theoretically, the largest and smallest must be neighbors and the
t ti i t f th th d i l t d b t th Thread Started
0˚ Δ=2.531
III
IVIII
180˚ Δ=1.942
starting point of the thread is located between them.
Conclusion 2:
radial cross section
270˚ Δ=1.384
* The starting point of the thread is located in quadrant IV.
* The helix is clockwise.
5NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering
360 degree view from
Methodology and setup based on a CCD and a Sight-pipe
360 degree view fromthe sight pipe Selected annular zone Reconstruct final
image
Stitching Line strip Define the annular Zone of each Frame
Extract and unwrap the annular Zone of each
Frame
Sight Pipe
Lens & Illumination
CCD
360-degree-view Line Scan Flow
Motion stages
Tilt Stages
LEDIllumination Internal Thread Measurement System
Conical Lens
Optical principle of the sight-PipeThe integrated sensor
6NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering
The integrated sensor
Measurements results
1. A smoothing filter followed by Prewitt filter is used to bring out edges.
2. LabVIEW Shape detection is used to extract average angle of threads.
3. A series of line profiles are generated perpendicular to the thread lines and peaks are detected.
7NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering
Discussion on Method 2 (3D Digitalization)
8NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering
Challenges on 3D Digitalization
Phase shifting could be achieved by using Light Modulation Technique. With digital light source we can fulfill the phase shift.
How to find the Sensitivity factor by changing the cylinder diameter as the reference plane moves.
The angle of the illumination and optical axis is either very small or immeasurable which could cause K unsolvable ( we may start with flat surface calibration. )
The misalignment of the sight pipe system and surface quality variationmay lead to errors in phase shifting measurement.
Next step: Prove if it is doable or not.
9NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering
The EndThe End
10NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering
Discussion on Method 2 (3D Digitalization)
11NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering
Challenges on 3D Digitalization
Phase shifting could be achieved by using Light Modulation Technique. With digital light source we can fulfill the phase shift.
How to find the Sensitivity factor by changing the cylinder diameter as the reference plane moves.
The angle of the illumination and optical axis is either very small or immeasurable which could cause K unsolvable ( we may start with flat surface calibration. )
The misalignment of the sight pipe system and surface quality variationmay lead to errors in phase shifting measurement.
Next step: Prove if it is doable or not.
12NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering
Engineering Research Center for
Reconfigurable Manufacturing Systems
Outlier Teeth Detection in Sprocketsp
Dr Reuven KatzDr. Reuven Katz Saikrishnan Ramachandran
March 13 2009March 13, 2009
The University of Michigan, College of Engineering
Goals
The Objective
To decide if there is an agreement to initiate a projectTo decide if there is an agreement to initiate a project
Project Goals
• Detect the presence of misaligned teeth i.e. outliers in sprocketsDetect the presence of misaligned teeth i.e. outliers in sprockets
• Measure in line the location and the extent of deformation for each outlier relative to its neighbors while the sprocket rotates
2NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering
Experimental setup
Optimet laser sensor
Sprocket
Aerotech rotaryAerotech rotary stage
3NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering
Suggested Method
• Measure the distance to tip of the teeth of a rotating sprocketMeasure the distance to tip of the teeth of a rotating sprocketusing a non-contact single-point laser range finder
• Each tooth is clearly observed as a bar (several points)
• Outliers are observed as peaks
4NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering
Evaluation of the method
Apriori
Rotating speed (sprocket) w = 300 rpm f = 5HzRotating speed (sprocket) w = 300 rpm f = 5Hz
Tooth width (sprocket) L = 5 mm
Diameter (sprocket) D = 175 mm
Data collection frequency (Optimet) fop = 3000 Hz
Number of points collected/revolution N = 3000/5 = 600
Number of points per tooth/revolution n = NL/ (πD) 3000 /(175 ) 5 45= 3000 /(175π) = ~5.45
= 5-6 points
Conclusion: Possible to be tested !!!
5NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering
Conclusion: Possible to be tested !!!
Stationary Experiment
Measurement of the profile of two sets of teeth for a stationary sprocket and linearly moving Optimet sensor: one containing all normal teeth (blue) and the second containing outliers (red)
Observation : 3 outliers detected whose deformations are ~0 35 mm 1mm and 0 21mm respectively
6NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering
~0.35 mm, 1mm and -0.21mm respectively
Evaluation of the method360 d t fil f th ti f th k t t l d360 degree rotary scan profile of the tip of the sprocket at a slow speed gives the outlier tooth location (w.r.t. starting point) and the deformation
Outliers are observed at teeth 5, 6 and 7 (w.r.t. start tooth) and the measured displacements are 0 21mm; 0 98mm and 0 35mm respectivelymeasured displacements are -0.21mm; 0.98mm and 0.35mm respectively
7NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering
Evaluation of the method
• To check the performance of the method, the number of collected points per tooth is down sampled from thousands to 5
• The outlier displacement values are calculated in each of the two cases
• Results are tabulated below
Displacement Thousands of points
5 random points
Outlier 1 - 0.2088 mm - 0.2107 mm
Outlier 2 0.9899 mm 0.9902 mm
Outlier 3 0 3585 mm 0 3622 mmOutlier 3 0.3585 mm 0.3622 mm
Note: negative deformation signifies inward deformation
8NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering
g g
Conclusion
1. The proposed method is an accurate way for the detection of outliers in sprocketsof outliers in sprockets
2. It is also accurate in estimating the deformation/displacement of outliers
3. Can be applied in an industrial application with a rotating sprocket
9NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering
Engineering Research Center for
Reconfigurable Manufacturing Systems
Valve Seat Gap Inspection
March13th, 2009
The University of Michigan, College of Engineering
Project Goal
Goals:• To develop a methodology to measure the small gap between valve
seat and cylinder head
I li t t h i th t bl hi h d t ti
y
Expected Deliverables:• In-line measurement technique that can enable high speed automatic
inspection
Work done:
• Proof on concept using a laser probe with a motorized periscope
2NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering
Infrared Valve Seat GappDetection System
Dan SimonQuality Network
Planned MaintenancePlanned [email protected]
Infrared Study of poorly “seated” exhaust Valve Seats
Seat “gaps” set @ .001, .003, g p @ , ,006 & .010 inches in L-6 aluminum head.
L-6 head warmed up w/ quartz lamp
Dan Simon (313) 324-5353 [ [email protected] ]
Exhaust Valve Seat w/ .010in gap @ 113F
48.0°C48
47
46.0°C
L6 Engine Head w/ .006 in gap in exhaust insert seat @ 118F Head Temperature
48.0°C48
47
46.0°C
Exhaust Valve seat w/ .003 in. gap @ 120F
49.9°C
49
48
47
46.0°C
Exhaust Valve Seat w/ .001 in. gap @ 114 F
47.0°C4747
46
45.0°C45
Exhaust Valve Seat w/ .001 in Gap @ 114 F(X 2 magnified)
47.0°C4747
46
45.0°C45