Stochastic simulation of stamping production – a Six …/media/Files/Autosteel/Great Designs in...
Transcript of Stochastic simulation of stamping production – a Six …/media/Files/Autosteel/Great Designs in...
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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