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Research Papers: Design Theory and Methodology

Comparing Strategies for Topologic and Parametric Rule Application in Automated Computational Design Synthesis1

[+] Author and Article Information
Corinna Königseder

Mem. ASME
Engineering Design and Computing Laboratory,
Department for Mechanical and Process Engineering,
ETH Zurich,
Zurich 8092, Switzerland
e-mail: ck@ethz.ch

Kristina Shea

Mem. ASME
Engineering Design and Computing Laboratory,
Department for Mechanical and Process Engineering,
ETH Zurich,
Zurich 8092, Switzerland
e-mail: kshea@ethz.ch

Contributed by the Design Theory and Methodology Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received March 19, 2015; final manuscript received September 3, 2015; published online November 4, 2015. Assoc. Editor: Andy Dong.

J. Mech. Des 138(1), 011102 (Nov 04, 2015) (12 pages) Paper No: MD-15-1234; doi: 10.1115/1.4031714 History: Received March 19, 2015; Revised September 03, 2015

Graph grammars are used for computational design synthesis (CDS) in which engineering knowledge is formalized using graphs to represent designs and rules that describe their transformation. Most engineering tasks require both topologic and parametric rules to generate designs. The research presented in this paper compares different strategies for rule application to combine topologic and parametric rules during automated design synthesis driven by a search process. The presented strategies are compared considering quantity and quality of the generated designs. The effect of the strategies, the selected search algorithm, and the initial design, from which the synthesis is started, are analyzed for two case studies: gearbox synthesis and bicycle frame synthesis. Results show that the effect of the strategy is dependent on the design task. Recommendations are given on which strategies to use for which design task.

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Figures

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Fig. 1

Overview of the method

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Fig. 2

Overview of the Burst algorithm

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Fig. 3

Schematic representation of the covered archive area (a) and archive progression (a)–(c)

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Fig. 4

Schematic representation of the rule set

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Fig. 5

Convergence of the Pareto sets using the simple Burst algorithm (10,000 iterations)

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Fig. 6

Convergence of the Pareto sets using the advanced Burst algorithm (10,000 iterations)

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Fig. 7

Visualizing dominant strategies in pairwise comparison. (Color version available online.)

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Fig. 8

Schematic representation of the bicycle frame synthesis rule set

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Fig. 9

Initial designs for bicycle frame synthesis case study

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Fig. 10

Convergence of the Pareto archive for the simple Burst algorithm (initial design: diamond frame)

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Fig. 11

Convergence of the Pareto archive for the advanced Burst algorithm (initial design: diamond frame)

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Fig. 12

Pairwise comparison of the strategies (initial design: diamond frame)

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Fig. 13

Convergence of the Pareto archive for the simple Burst algorithm (initial design: void frame)

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Fig. 14

Convergence of the Pareto archive for the advanced Burst algorithm (initial design: void frame)

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Fig. 15

Pairwise comparison of the strategies (initial design: void frame)

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Fig. 16

Pairwise comparison of designs generated when the synthesis is started from the diamond frame (shaded portion of the pie chart) and the void frame (white portion of the pie chart)

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Fig. 17

Example bicycle frames generated when the synthesis process is started from the diamond frame (left) and the void frame (right)

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