Teresa Primoa and Barbara Manisia

aDepartment of Engineering Innovation, University of Salento, Italy

Introduction

Component families and shape parameters

definition

Reference model

Key Performance Indexes

Engineering intelligence model and

data analysis

Conclusions and Further Developments

A brief introduction to the state-of-the-art

Classification of different components based on

specific parameters

Description of the test case for the methodology

application

Process evaluation through performance indexes

definition

KPI application to reference model and discussion

of the obtained results

Summary and upshots

Introduction

Components families and

shape parameters definition

Reference model

Key Performance Indexes

Engineering intelligence

model and data analysis

Conclusions and Further

Developments

Reduction of the costs

Potential sources of defects to reduce

Improve part quality vs complexity

Forming forces

Stress, strain and temperature distributions

Material flow

In sheet metal forming, modeling and simulation can be used for many purposes

KEY PERFORMANCE INDEX

(KPI)

COMPONENT FAMILY

DEFINITION

Introduction

Component families and

shape parameters definition

Reference model

Key Performance Indexes

Engineering intelligence

model and data analysis

Conclusions and Further

Developments

3 1

2

4

5

6

7

8

SQUAT SHAPES:

high drawing

depth

SE

CT

ION

DE

VE

LO

PM

EN

T

SH

AP

ES

: d

ev

elo

pm

en

t o

f a

co

nst

an

t se

cti

on

on

a

lon

git

ud

ina

l a

xis

Introduction

Component families and

shape parameters definition

Reference model

Key Performance Indexes

Engineering intelligence

model and data analysis

Conclusions and Further

Developments

Lblank = 440

Blank

Punch

Blankholder

310 300

700

Hadd = 240

Lprod

Hprod

Final Component

Numerical model of the industrial test case (“SELLA” ) and investigated process

parameters

Thickness 0.8 mm 1 mm 1.2 mm

Materials ASM5532 Al2024 T6

BHF 110 tons 130 tons 150 tons

Die Radius Rd1 = 25 mm Rd2 = 32.5 mm

Punch Radius Rp = 70 mm

Introduction

Component families and

shape parameters definition

Reference model

Key Performance Indexes

Engineering intelligence

model and data analysis

Conclusions and Further

Developments

Shape Parameters calculation:

SF1=Hprod/Lprod 200/350 = 0.6

SF2=Hadd/Lblank 240/440 = 0.5

PRR=Rp/Thick

70/0.8 = 87.5

70/1.0= 70

70/1.2 = 58

DRR=Rd/Thick

25/0.8 = 31 32.5/0.8 = 41

25/1 = 25 32.5/1 = 32.5

25/1.2 = 21 32.5/1.2 = 27

Where:

Hprod: maximum drawing

depth of the final product;

Hadd: maximum drawing

depth of the punch tool with

addendum;

Rp: punch radius;

Rm: die radius;

Thick: initial blank

thickness

Lblank = 440

Blank

Punch

Blankholder

310 300

700

Hadd = 240

Lprod

Hprod

Final Component

Introduction

Component families and

shape parameters definition

Reference model

Key Performance Indexes

Engineering intelligence

model and data analysis

Conclusions and Further

Developments

where: diT =

0

1it

t

æ ö-ç ÷

è ø

Fracture KPI

Wrinle KPI

Loose Metal KPI

Thickness KPI

Introduction

Component families and

shape parameters definition

Reference model

Key Performance Indexes

Engineering intelligence

model and data analysis

Conclusions and Further

Developments

Process responses evaluation

Introduction

Component families and

shape parameters definition

Reference model

Key Performance Indexes

Engineering intelligence

model and data analysis

Conclusions and Further

Developments

BARLINE NUMBER OF PROJECT VS DRR

Introduction

Component families and

shape parameters definition

Reference model

Key Performance Indexes

Engineering intelligence

model and data analysis

Conclusions and Further

Developments

Die Radius Ratio: DRR = Rm/Thick

SL-07

SL-08

SL-09

SL-13

SL-14

SL-15 prr= 58

SL-01

SL-02

SL-03

SL-31

SL-32

SL-33

prr= 87.5

SL-25

SL-26

SL-27

prr= 87.5

prr= 70

Fra

ctu

res/L

oo

se prr= 58

SL-19

SL-20

SL-21

prr= 70

Introduction

Component families and

shape parameters definition

Reference model

Key Performance Indexes

Engineering intelligence

model and data analysis

Conclusions and Further

Developments

Punch Radius Ratio: PRR = Rp/Thick

SL-13

SL-14

SL-15

SL-31

SL-32

SL-33

drr= 21

drr= 27

SL-07

SL-08

SL-09

SL-25

SL-26

SL-27

drr= 25

drr= 32.5

SL-01

SL-02

SL-03

SL-19

SL-20

SL-21

drr= 31

drr= 41

Th

ick

Fra

ctu

res/L

oo

se

Introduction

Component families and

shape parameters definition

Reference model

Key Performance Indexes

Engineering intelligence

model and data analysis

Conclusions and Further

Developments

Punch Radius Ratio: PRR = Rp/Thick

Fra

ctu

res K

PI

Wri

nk

les/

Lo

ose

Me

tal/

Th

ick

ne

ss V

ari

ati

on

SL-16

SL-17

SL-18

SL-34

SL-35

SL-36

SL-10

SL-11

SL-12

SL-28

SL-29

SL-30

SL-04

SL-05

SL-06

SL-22

SL-23

SL-24

The presented work illustrates how it has been developed a new approach that allows: To support users during the process design development

phase in the generated data management. In fact different data aggregation rules have been implemented. The authors have defined a set of Key Performance Indexes (KPI) which help the evaluation, generally made by the designers, during the post-processing about the feasibility of the analyzed solutions.

Objective verification of the process parameters influence

on the product feasibility. The structuring and aggregation of the generated data allow to the same data to be a reference base for the performances analysis of the analyzed test case.

The proposed approach, implemented in a numerical

environment, can be also applied with a better effectiveness in a experimental testing scenario.

Introduction

Component families and

shape parameters definition

Reference model

Key Performance Indexes

Engineering intelligence

model and data analysis

Conclusions and Further

Developments