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Design Exploration. Christopher A. Mattson Department of Mechanical Engineering Brigham Young University. MeEn 579 – Global Product Development MeEn 576 – Product Design MeEn 497 – Innovation & Entrepreneurship ( interdisc ) MeEn 476 – Product and Process Development 2
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Design Exploration Christopher A. Mattson Department of Mechanical Engineering Brigham Young University
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
PAIN: Museums need data about customer habits of their strategic decisions are based on hypotheses. Optimization inEarly Design
Part 1 Design Space Part 2 Problem Formulation Part 3 Pareto Traversing
PART 1 Design Space
Mattson and Sorensen, Fundamentals of Product Development, 2013.
PAIN: Museums need data about customer habits because most of their strategic decisions are based on hypotheses. Desirable & Transferable
What’s happening in the design space? PAIN: Museums need data about customer habits because most of their strategic decisions are based on hypotheses.
What’s happening in the design space? PAIN: Museums need data about customer habits because most of their strategic decisions are based on hypotheses.
What’s happening in the design space? PAIN: Museums need data about customer habits because most of their strategic decisions are based on hypotheses.
What’s happening in the design space? PAIN: Museums need data about customer habits because most of their strategic decisions are based on hypotheses.
What’s happening in the design space? PAIN: Museums need data about customer habits because most of their strategic decisions are based on hypotheses.
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.
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.
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.
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.
PAIN: Museums need data about customer habits because most of their strategic decisions are based on hypotheses.
PART 2 Problem Formulation
interdisciplinary multidisciplinary monodisciplinary
PAIN: Museums need data about customer habits because most of their strategic decisions are based on hypotheses.
MultipleObjectives PAIN: Museums need data about customer habits because most of their strategic decisions are based on hypotheses.
InterconnectedObjectives PAIN: Museums need data about customer habits because most of their strategic decisions are based on hypotheses.
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.
Generic Formulation subjectto
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
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
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.
Curtis, Hancock, and Mattson, Design Space Exploration with a Dynamic Opt. Formulation, Research in Eng. Design, 2013
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
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
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
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
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
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
subject to subject to where
subject to where
Novelty Preferred Variety Quality 45 Curtis, Mattson, Lewis, and Hancock, Divergent Exploration in Design … Structural and Multidisciplinary Opt., 2013.
Novelty where 46
Novelty where 47
Novelty where 48
Quality where is the aggregate objective function value 50