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Stochastic simulation of stamping production –

a Six Sigma paradigm

Yu-Wei Wang, Tony ChangSeverstal North America

Cedric XiaFord Motor Company

Kidambi KannanAutoForm Engineering USA

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Outline

• Material sourcing partnership with OEMs

• Further value-added opportunities• Illustrative study

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Partnership with OEMs

Styling/ Design Feas.

Grade/gagevalidation

Process validation

Blank size optimization

Homeline tryout

Production support

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Partnership with OEMs

Performance/weight Feasibility

Cost

Styling/ Design Feas.

Grade/gagevalidation

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Process validation

Blank size optimization

• Feasibility support• in-house processing• supplier processing

• Cost-balanced blank shape• developed & nested?• sheared?

Partnership with OEMs

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Partnership with OEMs

Homeline tryout

Support and Certification

• material grade/gage verification• circle-grids, strain/thinning measurement• tryout feasibility certification• process/die countermeasures identification

Circle grid Draw-in

Simulation support

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Partnership with OEMs

Production support

• in-production scrap reduction/elimination

• continuous improvement• part quality• process / die • material cost

• reference panel

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Value add opportunities

Further value-added opportunities?

• Robust feasibility

• Process- and cost-optimal blank

• Virtual reference panel

• Scrap reduction / elimination

• Part quality guarantee

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Styling/ Design Feas.

Grade/gagevalidation

Performance/weight Feasibility?

Cost

? Is design robust to gageand spec variations for selected grade

Value add opportunities

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Process validation

Blank size optimization

• Feasibility support• in-house processing• supplier processing

• Cost-balanced blank shape• developed & nested?• sheared?

• Is process just adequate?

• Is process robust? “Capable” Cpk?• else, countermeasure?

• Is blank cost- / robustness-optimal?

Value add opportunities

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Homeline tryout

Support and Certification

• material & gage verification• circle-grids, panel measurement• tryout support• tryout feasibility certification

• is the certified process truly capable?• scrap estimate?• scrap countermeasure before

start-of-production?• virtual process signature – reference panel

Circle grid Draw-in

Simulation support

Value add opportunities

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Production support

• in-production scrap reduction/elimination

• continuous improvement• part quality• process / die • material cost

• reference panel

• scrap reduced / eliminated?• quick / intuitive root-cause analysis• quick eval of countermeasure alternatives• validated / data-driven cost improvement

decisions!

Value add opportunities

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Recent study

• Wheelhouse draw die – product formability issues

• Modified to countermeasure local formability

• Scrap generated in production

• Root cause and countermeasure?Further cost improvement opportunities?

• Quality improvement / robustnessCost savings

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Recent studyProduct formability issues

Formability

Thinning

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Recent studyFormability countermeasure

Formability

Embossment wall angles & corners

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Recent studyFormability countermeasure

One-hit quality metrics – Safe Panel!

But –

Is this good for production?

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Recent studySimulation of Stamping Production

AutoForm-Sigma

Multiplesimulations

SigmaNominal process

Processvariations

Materialvariations

Yield

Tensile

Cpk

Root cause

Process window

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Recent studyProduction scrap - estimate

20% thinning limit

Production scrap~

13%

AutoForm-Sigma - Stamping robustness

Acceptable thinning

Excessive thinning

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Recent studyScrap countermeasure?

First – root cause• Variations - Material? Thickness? Lube?• Beads?• Blank shape / size?

Where was the intuition? “Material out-of-spec”

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Recent studyCountermeasure and improvement?

Design study using AutoForm-Sigma

• Objectives –• can process be made robust against “noise”?• simultaneously improve overall quality, cost?

• “production” study• simultaneous “noise” and “design” parameters

•“noise” – uncontrollable, just happens …• material specs, gage, lube

• “design” – controllable, useful for improvement• blank size / shape• bead strength

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Blank outline

Blank shape range

Bead force range

Material specs, Gage, Lube variations

“Noise”

“Noise” parameters – always present “Design” parameters – use to improve

Recent studyCountermeasure and improvement?

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“Design” & “noise”influencers

1

2 3

6

5

4

Locations for quality improvement

Pareto for controlling factors

Recent studyCountermeasure and improvement?

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80% of range from outer sideBottom blank edge (“Bot-Bl”)6

Less than 0.5Bead1 (“Bd1”)5

Less than 0.5Bead1 (“Bd1”)4

80% of range from inner sideTop blank edge (“Top-Bl”)3

80% of range from outer sideBottom blank edge (“Bot-Bl”)2

Less than 0.5Bead1 (“Bd1”)1

Ideal parameter rangeDominant parameterZone

“Design” & “noise”influencers

Recent studyCountermeasure and improvement?

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“Process window” for improvement

Avoid blank edge here!

• Blank edge within windowfor adequate formability

• Blank edge farther insideto improve material cost!

“Top-Bl”study range

“Bottom-Bl”study range

Recent studyCountermeasure and improvement?

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Optimal process

• Improved blank

• Modified beads

Recent studyCountermeasure and improvement?

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Recent studyWhat is the improvement?

Blank piece cost -$ 1.84 Improved - $1.98

Original~ $0.14 savings / blank

Cost

OriginalUtilization ~ 79%

ImprovedUtilization ~ 81%

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Recent studyWhat is the improvement?

20% thinning limit

Estimated scrap~

< 1%

Quality

Acceptable thinning

Excessive thinning

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Conclusions

New simulation paradigm – AutoForm-Sigma

• “Virtual production” – not just “virtual stamping”

• Enables upfront focus on scrap reduction / elimination

• Common goals for product / process engineering and stamping production:

• robust production, Cpk• guaranteed quality through production life

• Application study - common-sense approach to cost-optimal, robust stamping process design

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Simulation of Stamping Production

AutoForm-Sigma

• Simulate quality variations due to material, lube, gage location, etc.• “noise” and “design” input variables• multiple simulations; fully parallel over network

• “Production” statistics for quality metrics – springback, thinning, etc.

• Root cause for quality spills• upfront countermeasure opportunity: avoid spills• in-production: identify & verify countermeasure, then implement

• Essential for successful springback compensation• stability of springback magnitude before compensation• meaningful compensation• repeatability after compensation