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Research Papers

Product Family Design Through Ontology-Based Faceted Component Analysis, Selection, and Optimization

[+] Author and Article Information
Ying Liu

Mem. ASME
e-mail: mpeliuy@nus.edu.sg

Soon Chong Johnson Lim

Student Mem. ASME
Mechanical Engineering,
National University of Singapore,
9 Engineering Drive 1,
Singapore 117576

Wing Bun Lee

Industrial and Systems Engineering,
Hong Kong Polytechnic University,
Hung Hom, Kowloon,
Hong Kong S.A.R., China

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the Journal of Mechanical Design. Manuscript received December 26, 2011; final manuscript received December 4, 2012; published online June 20, 2013. Assoc. Editor: Timothy W. Simpson.

J. Mech. Des 135(8), 081007 (Jul 20, 2013) (17 pages) Paper No: MD-11-1525; doi: 10.1115/1.4023632 History: Received December 26, 2011; Revised December 04, 2012; Accepted December 10, 2012

Product family design (PFD) is a widely adopted strategy for product realization, especially when design requirements are diversified and multi-faceted. Due to ever-changing customer needs and the increasingly complex and integrated product design structure, PFD and its optimization have been concerned more about a rapid and contextual product analysis and variant derivation based on a multi-objective optimization scheme subject to design concerns, which are often cross disciplinary, such as product service, carbon footprint, user experience, esthetics, etc. Existing PFD modeling approaches, which are primarily structured using component attributes and assembly relationships, possess notable limitations in representing complex component and design relationships. Hence, it has restricted comprehensive PFD analysis in an agile and contextual manner. Previously, we have studied and demonstrated the feasibility of using ontology for product family modeling and have suggested a framework of faceted information search and retrieval for product family design. In this paper, several new perspectives towards PFD based on ontology modeling are presented. Firstly, new metrics of ontology-based commonality that better reveal conceptual similarity under various design perspectives are formed. Secondly, faceted concept ranking is proposed as a new ranking approach for ontology-based component search under complex and heterogeneous design requirements. Thirdly, using these ranked results, a platform selection approach that considers a maximum aggregated ranking with a minimal platform modification among various platform choices is researched. From the selected platform and the newly proposed metrics, a modified multi-objective evolutionary algorithm with an embedded feature of configuration incompatibility check is studied and deployed for the optimal selection of components. A case study of PFD using four laptop computer families is reported as our first attempt to showcase how faceted component analysis, selection, and optimization can be accomplished based on the proposed family ontology.

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Figures

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Fig. 1

Proposal of a new perspective of product family design using ontology

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Fig. 2

Chromosome representation

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Fig. 3

Experimental flow procedures for Multiobjective product family optimization

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Fig. 4

Screenshot of a semantically annotated MFPFO in Protégé ontology editor

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Fig. 5

Aggregated faceted score values of different product families under multiple facets

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Fig. 8

Generated laptop configurations under different CSI and cost settings

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Fig. 9

Generated laptop configurations generated under different fSI and cost settings

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Fig. 6

Non-dominated Pareto solution sets at different configuration similarity indices

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Fig. 7

Non-dominated Pareto Solution sets at different faceted similarity indices for “HDFunction”

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