Engineering Data Management for Metal Forming Process

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Engineering Data Management for Metal Forming Process

  1. 1. Teresa Primoa and Barbara ManisiaaDepartment of Engineering Innovation, University of Salento, Italy
  2. 2. Introduction A brief introduction to the state-of-the-art Component families Classification of different components based onand shape parameters specific parameters definition Description of the test case for the methodology Reference model application Key Performance Process evaluation through performance indexes Indexes definition Engineering KPI application to reference model and discussionintelligence model and of the obtained results data analysis Conclusions and Summary and upshotsFurther Developments
  3. 3. In sheet metal forming, modeling and simulation can be used for many purposes Introduction Material flow Components families andshape parameters definition Stress, strain and temperature distributions KEY PERFORMANCE INDEXReference model (KPI) Forming forcesKey Performance Indexes Improve part quality vs complexity Engineering intelligence Potential sources of defects to reduce model and data analysis COMPONENT FAMILY DEFINITION Reduction of the costsConclusions and Further Developments
  4. 4. SHAPES: development of a SECTION DEVELOPMENT Introduction 1 3 6 constant section on a Component families and longitudinal axisshape parameters definitionReference model SQUAT SHAPES: 4Key Performance high drawing 2 8 depth Indexes Engineering intelligence model and data analysisConclusions and Further 7 Developments 5
  5. 5. Numerical model of the industrial test case (“SELLA” ) and investigated process Introduction parameters Thickness 0.8 mm 1 mm 1.2 mm Component families and Materials ASM5532 Al2024 T6shape parameters definition BHF 110 tons 130 tons 150 tons Die Radius Rd1 = 25 mm Rd2 = 32.5 mm Punch Radius Rp = 70 mmReference modelKey Performance Indexes Engineering intelligence model and data analysis Hprod LprodConclusions and Further Developments Final Component
  6. 6. Shape Parameters calculation: Introduction SF1=Hprod/Lprod 200/350 = 0.6 Where: Hprod: maximum drawing Component SF2=Hadd/Lblank 240/440 = 0.5 depth of the final product; families and Hadd: maximum drawingshape parameters 70/0.8 = 87.5 definition depth of the punch tool with PRR=Rp/Thick 70/1.0= 70 addendum; Rp: punch radius; 70/1.2 = 58 Rm: die radius;Reference model 25/0.8 = 31 32.5/0.8 = 41 Thick: initial blank thickness DRR=Rd/Thick 25/1 = 25 32.5/1 = 32.5Key Performance Indexes 25/1.2 = 21 32.5/1.2 = 27 310 Engineering intelligence model and data analysisConclusions and Hprod Further Lprod Developments Final Component
  7. 7. Introduction Fracture KPI Component families andshape parameters definitionReference model Wrinle KPIKey Performance Indexes Engineering intelligence model and data analysis Loose Metal KPIConclusions and æt ö where: d T = ç i - 1÷ i Further Developments è t0 ø Thickness KPI
  8. 8. Process responses evaluation Introduction Component families andshape parameters definitionReference modelKey Performance Indexes Engineering intelligence model and data analysisConclusions and Further Developments
  9. 9. Introduction BARLINE NUMBER OF PROJECT VS DRR Component families andshape parameters definitionReference modelKey Performance Indexes Engineering intelligence model and data analysisConclusions and Further Developments
  10. 10. Introduction SL-01 SL-02 prr= 87.5 SL-03 Component families and SL-31shape parameters SL-32 prr= 58 SL-33 Fractures/Loose definitionReference model SL-25 SL-26 prr= 70 SL-27 SL-07 SL-08 prr= 70Key Performance SL-09 Indexes SL-13 SL-14 prr= 58 SL-19 SL-15 SL-20 prr= 87.5 SL-21 Engineering intelligence model and data analysisConclusions and Further Developments Die Radius Ratio: DRR = Rm/Thick
  11. 11. Introduction Component families andshape parameters Fractures/Loose definition SL-07 SL-08 drr= 25 SL-09 SL-01 SL-02 drr= 31 SL-25 SL-03 SL-26 drr= 32.5Reference model Thick SL-27 SL-19 SL-20 drr= 41 SL-13 SL-21 SL-14 SL-15 drr= 21Key Performance SL-31 Indexes SL-32 drr= 27 SL-33 Engineering intelligence model and data analysisConclusions and Further Developments Punch Radius Ratio: PRR = Rp/Thick
  12. 12. Introduction Wrinkles/Loose Metal/Thickness Variation SL-10 SL-11 SL-12 Component families andshape parameters SL-28 definition SL-29 SL-30 Fractures KPIReference model SL-16 SL-17 SL-04 SL-18 SL-05 SL-06Key Performance Indexes SL-34 SL-35 SL-22 SL-36 SL-23 SL-24 Engineering intelligence model and data analysisConclusions and Further Developments Punch Radius Ratio: PRR = Rp/Thick
  13. 13. The presented work illustrates how it has been developed a Introduction new approach that allows: Component  To support users during the process design development families and phase in the generated data management. In fact differentshape parameters definition data aggregation rules have been implemented. The authors have defined a set of Key Performance Indexes (KPI) which help the evaluation, generally made by theReference model designers, during the post-processing about the feasibility of the analyzed solutions.Key Performance  Objective verification of the process parameters influence Indexes on the product feasibility. The structuring and aggregation of the generated data allow to the same data to be a Engineering reference base for the performances analysis of the intelligence model and data analyzed test case. analysis  The proposed approach, implemented in a numericalConclusions and environment, can be also applied with a better Further Developments effectiveness in a experimental testing scenario.

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