A variation-based method for product family design

Raviraj U. Nayak, Wei Chen, Timothy W. Simpson

Research output: Contribution to journalArticle

199 Scopus citations

Abstract

Product family design entails all of the challenges of product design while adding the complexity of coordinating the design of multiple products in an effort to maximize commonality across a set of products without compromising their individual performance. This paper presents the Variation-Based Platform Design Method (VBPDM) for product family design, which aims to satisfy a range of performance requirements using the smallest variation of the product designs in the family. In the first stage of the VBPDM, the product platform around which the product family is to be developed is identified. The product platform is common to all of the products in the family and represents the maximum standardization possible considering the variety of performance requirements that must be satisfied. To satisfy the range of performance requirements for the product family, a ranged set of solutions is found using variation-based modeling. A compromise Decision Support Problem (DSP) is formulated to solve the tradeoff between satisfying the variety requirement and maximizing platform commonality. Platform commonality is achieved by introducing a commonality goal that seeks to minimize the deviation of the input design variables while satisfying the range of performance requirements. Those design variables that show small deviations are held constant to form the product platform. In the second stage of the VBPDM, each individual product is designed around the common platform such that the functional requirements for each product in the family are best satisfied. As an example, the proposed method is used to develop a family of universal electric motors designed to meet a range of torque requirements. The results are compared against previous work on the same example.

Original languageEnglish (US)
Pages (from-to)65-81
Number of pages17
JournalEngineering Optimization
Volume34
Issue number1
DOIs
StatePublished - Jan 2002

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Control and Optimization
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering
  • Applied Mathematics

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