Technical Briefs

Application of Multiparameter Optimization for Robust Product Design

[+] Author and Article Information
Douglas K. Herrmann

 Xerox Corporation, USA, 800 Phillips Road, Mailstop 0207-01Z, Webster, NY 14580doug.herrmann@xerox.com

J. Mech. Des 131(2), 024501 (Jan 07, 2009) (6 pages) doi:10.1115/1.3042162 History: Received May 27, 2008; Revised October 28, 2008; Published January 07, 2009

Taguchi optimization or robust design is an effective way to balance rigorous statistical testing and compressed time schedules when engineering a product design. In an effort to define the specifications of the parameters that will eventually control the product output, it is necessary to first identify those parameters that will most likely have the greatest effect on the performance of the system. Once the parameters have been narrowed down, it is then the job of the testing to identify the levels of each parameter to ensure robust performance of the system. Without the efficiencies of the designed experiments described as robust design, it would be impossible within the constraints of a tight design schedule to properly optimize a system design for reliable performance. The usefulness of the Taguchi optimization is especially important when a design will be subjected to a wide variety of noises. It is for this reason that Taguchi techniques fit well in the world of paper handling product design. This paper discusses how these techniques were used to optimize a production printer finishing module. It steps through the process from identifying the parameters and setting up the test to the additional evaluation of parameters to meet the design constraints of the physical hardware.

Copyright © 2009 by American Society of Mechanical Engineers
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Figure 1

Set of multiple sheets of paper—compiled and dropped (2,13): (a) compiling, (b) LE drop, and (c) TE drop

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Figure 2

Isometric views of the compile, drop, and stacking mechanisms

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Figure 3

Test setup showing inner (L9) parameter array and outer (L8) noise array

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Figure 4

S/N ratios for each level of the design parameters

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Figure 5

Data from the confirmation run



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