Post on 25-Jun-2015
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