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Design Exploration

Design Exploration

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Design Exploration

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  1. Design Exploration Christopher A. Mattson Department of Mechanical Engineering Brigham Young University

  2. MeEn 579 – Global Product Development MeEn 576 – Product Design MeEn 497 – Innovation & Entrepreneurship (interdisc) MeEn 476 – Product and Process Development 2 MeEn 475 – Product and Process Development 1 MeEn 373 – Engineering Computing MeEn 372 – Machine Design

  3. PAIN: Museums need data about customer habits of their strategic decisions are based on hypotheses. Optimization inEarly Design

  4. Design Exploration

  5. Part 1 Design Space Part 2 Problem Formulation Part 3 Pareto Traversing

  6. PART 1 Design Space

  7. Mattson and Sorensen, Fundamentals of Product Development, 2013.

  8. PAIN: Museums need data about customer habits because most of their strategic decisions are based on hypotheses. Desirable & Transferable

  9. What’s happening in the design space? PAIN: Museums need data about customer habits because most of their strategic decisions are based on hypotheses.

  10. What’s happening in the design space? PAIN: Museums need data about customer habits because most of their strategic decisions are based on hypotheses.

  11. What’s happening in the design space? PAIN: Museums need data about customer habits because most of their strategic decisions are based on hypotheses.

  12. What’s happening in the design space? PAIN: Museums need data about customer habits because most of their strategic decisions are based on hypotheses.

  13. What’s happening in the design space? PAIN: Museums need data about customer habits because most of their strategic decisions are based on hypotheses.

  14. Concept Set Quantity Variety Novelty Quality PAIN: Museums need data about customer habits because most of their strategic decisions are based on hypotheses. J.J.Shah, S.M. Smith, and N. Vargas-Hernandez. Metrics for Measuring Ideation Effectiveness. Design Studies, 2003.

  15. Quantity in the Concept Set Low Quantity High Quantity PAIN: Museums need data about customer habits because most of their strategic decisions are based on hypotheses.

  16. Variety in the Concept Set Low Variety High Variety PAIN: Museums need data about customer habits because most of their strategic decisions are based on hypotheses.

  17. Novelty of the Concept Set Low Novelty High Novelty PAIN: Museums need data about customer habits because most of their strategic decisions are based on hypotheses.

  18. PAIN: Museums need data about customer habits because most of their strategic decisions are based on hypotheses.

  19. PART 2 Problem Formulation

  20. interdisciplinary multidisciplinary monodisciplinary

  21. PAIN: Museums need data about customer habits because most of their strategic decisions are based on hypotheses.

  22. MultipleObjectives PAIN: Museums need data about customer habits because most of their strategic decisions are based on hypotheses.

  23. InterconnectedObjectives PAIN: Museums need data about customer habits because most of their strategic decisions are based on hypotheses.

  24. Concept Set Quantity Variety Novelty Quality PAIN: Museums need data about customer habits because most of their strategic decisions are based on hypotheses. J.J.Shah, S.M. Smith, and N. Vargas-Hernandez. Metrics for Measuring Ideation Effectiveness. Design Studies, 2003.

  25. Generic Formulation subjectto

  26. Strategy 1 • Formulatean aggregate objective function that captures preference • Weighted Sum (WS) method • Compromise Programming (CP) • Goal Programming (GP) • Physical Programming (PP) • Convergeon a single Pareto solution

  27. Strategy 2 • Diverge: Obtain many Pareto solutions • WS, CP, PP methods • e-inequality Constraint method • Normal Boundary Intersection • Normal Constraint method • Converge:Choose the most attractive solution

  28. NC Method Steps • Obtain anchor points • Construct Utopia Line(blue) • Generate points on utopia line • Construct Normal Line(orange) through point on utopia line • Reduce feasible space • Minimize m2 • Repeat Steps 4-6 for all points on utopia line Messac, Ismail-Yahaya, and Mattson, The Normalized Normal Constraint Method… Structural and Multidisciplinary Opt., 2003.

  29. Curtis, Hancock, and Mattson, Design Space Exploration with a Dynamic Opt. Formulation, Research in Eng. Design, 2013

  30. Image Source: hitachipowertools.com

  31. Concept 1 Concept 2 Concept 3 Concept 4 Concept 5 I G T M W S = Impact mechanism = Bevel gears = Trigger = Motor = Counter weight = Spur gears

  32. Model Inputs • Location: Impact, motor • Type: Motor, gear set • Size:Shafts, gear set • Center of Mass • Total Weight • Total Cost • Torque supplied to impact • Speed supplied to impact • Maximum Stress Model Outputs 37

  33. Model Inputs • Location: Impact, motor • Type: Motor, gear set • Size:Shafts, gear set • Center of Mass • Total Weight • Total Cost • Torque supplied to impact • Speed supplied to impact • Maximum Stress Model Outputs 38

  34. Model Inputs • Location: Impact, motor • Type: Motor, gear set • Size:Shafts, gear set • Center of Mass • Total Weight • Total Cost • Torque supplied to impact • Speed supplied to impact • Maximum Stress Model Outputs 39

  35. Model Inputs • Location: Impact, motor • Type: Motor, gear set • Size:Shafts, gear set • Center of Mass • Total Weight • Total Cost • Torque supplied to impact • Speed supplied to impact • Maximum Stress Model Outputs 40

  36. Model Inputs • Location: Impact, motor • Type: Motor, gear set • Size:Shafts, gear set • Center of Mass • Total Weight • Total Cost • Torque supplied to impact • Speed supplied to impact • Maximum Stress Model Outputs 41

  37. 42

  38. subject to subject to where

  39. subject to where

  40. Novelty Preferred Variety Quality 45 Curtis, Mattson, Lewis, and Hancock, Divergent Exploration in Design … Structural and Multidisciplinary Opt., 2013.

  41. Novelty where 46

  42. Novelty where 47

  43. Novelty where 48

  44. Preferred Variety 49

  45. Quality where is the aggregate objective function value 50