Effective Product Family Design Using Preference Aggregation

[+] Author and Article Information
Zhihuang Dai

Department of Mechanical & Industrial Engineering, University of Illinois at Chicago, Chicago, IL 60607

Michael J. Scott1

Department of Mechanical & Industrial Engineering, University of Illinois at Chicago, Chicago, IL 60607mjscott@uic.edu

Note that although the equations in (7) are correct, all of the results presented in this paper rely on an incorrect expression for air gap area; the results are thus comparable, but the torque calculation is not physically meaningful. All methods compared use the same results.


Corresponding author.

J. Mech. Des 128(4), 659-667 (Oct 11, 2005) (9 pages) doi:10.1115/1.2197835 History: Received June 21, 2005; Revised October 11, 2005

The development of product families, groups of products that share a common platform, is one way to provide product variety while keeping design and production costs low. The design of a product platform can be formulated as a multicriteria optimization problem in which the performances of individual products trade off against each other and against the objective of platform standardization. The problem is often solved in two stages: one to determine the values of the shared platform variables and a second to optimize the product family members with respect to specific targets. In the first stage, it is common to target the mean and variability of performance when fixing the values of platform variables. This paper contributes three new methods for platform development. The new methods are demonstrated on an electric motor example from the platform design literature, and the results are compared to those from existing methods. First, a preference aggregation method is applied to aggregate the multiple objectives into a single overall objective function. On the example problem, this approach gives superior results to existing techniques. Second, an alternative method that targets the minimum and maximum of the range of performance across the platform, instead of the mean and standard deviation, is proposed and shown to succeed where the existing method may fail. Third, a single-stage optimization approach which solves for both platform and nonplatform variables in a single pass is presented. This method delivers notably superior performance on the example problem but will, in general, incur greater computational expense.

Copyright © 2006 by American Society of Mechanical Engineers
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Grahic Jump Location
Figure 1

A case in which targeting the mean and variation of y does not capture all y requirements

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Figure 3

Preference functions for mean and variation of torque

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Figure 4

Preference functions for minimum and maximum torque

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Figure 2

Preference functions for efficiency and mass



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