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Research Papers

Mining Functional Model Graphs to Find Product Design Heuristics With Inclusive Design Illustration

[+] Author and Article Information
Shraddha Sangelkar

Department of Mechanical Engineering,
School of Engineering,
242 Burke Center,
5101 Jordan Road,
Penn State Erie,
The Behrend College,
Erie, PA 16563-1701
e-mail: sangelkar@psu.edu

Daniel A. McAdams

Department of Mechanical Engineering,
3123 TAMU,
Texas A&M University,
College Station, TX 77843
e-mail: dmcadams@tamu.edu

Contributed by the Design Engineering Division of ASME for publication in the JOURNAL OF COMPUTERS AND INFORMATION DIVISION IN ENGINEERING. Manuscript received October 18, 2012; final manuscript received September 5, 2013; published online October 22, 2013. Editor: Bahram Ravani.

J. Comput. Inf. Sci. Eng 13(4), 041008 (Oct 22, 2013) (11 pages) Paper No: JCISE-12-1190; doi: 10.1115/1.4025469 History: Received October 18, 2012; Revised September 05, 2013

Engineering design heuristics offer the potential to improve the design process and resultant designs. Currently, heuristics are empirically derived by experts. The goal of this paper is to automate the heuristics generation process. Functional modeling, a well-established product representation framework, is applied in this research to abstract the intended functionality of a product. Statistically significant heuristics, extracted from a database of functional models, serve as design suggestions or guidelines for concept generation. The heuristics can further be applied to automate portions of the concept generation process. Prior research efforts in automated concept generation rely heavily on the design repository. The repository needs to be appended for broader categories of design problems, and, at the same time, a tool for quick analysis of the expanded repository is required. An automated heuristic extraction process has the capability to efficiently mine the updated repositories and find new heuristics for design practice. A key objective of this research is to develop design heuristics applicable in the diverse and challenging domain of inclusive design. The research applies graph theory for mathematical representation of the functional model, graph visualization for comprehending graphs, and graph data mining to extract heuristics. The results show that the graphical representation of functional models along with graph visualization allows quick updates to the design repository. In addition, we show that graph data mining has the capability to efficiently search for new design heuristics from the updated repository.

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References

Figures

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Fig. 1

Actionfunction diagram comparison of a can opener product pair [49]

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Fig. 2

The rule generation process to study the inclusive design characteristics [48]

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Fig. 3

Modified data analysis with graphical representation and frequent-pattern search to study the inclusive design characteristics

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Fig. 4

The bipartite graph representation of a can opener product pair comparison

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Fig. 5

Node labeling and adjacency matrix for the can opener example, where M, F, and N denote morphological change, functional change, and no change, respectively

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Fig. 6

Visualization of the input data for can opener example along with node numbering

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Fig. 7

Sample output from PAFI's FSG algorithm

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Fig. 8

Visualization for the sample output t # 2-21

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Fig. 9

Application of a frequent subgraph as design heuristic for inclusive design

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Fig. 10

Example of a rule found both by graph data mining and association rule mining

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Fig. 11

Example of a subgraph t # 8-0 of size 8 along with its visualization

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