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RESEARCH PAPERS

The Mechanisms by Which Adaptive One-factor-at-a-time Experimentation Leads to Improvement

[+] Author and Article Information
Daniel D. Frey1

Department of Mechanical Engineering and Engineering Systems Division, Massachusetts Institute of Technology, Room 3-449D, Cambridge, MA 02139danfrey@mit.edu

Rajesh Jugulum

Department of Mechanical Engineering, Massachusetts Institute of Technology, Room 3-449G, Cambridge, MA 02139jrajesh@mit.edu

1

Corresponding author.

J. Mech. Des 128(5), 1050-1060 (Aug 31, 2005) (11 pages) doi:10.1115/1.2216733 History: Received April 25, 2005; Revised August 31, 2005

This paper examines mechanisms underlying the phenomenon that, under some conditions, adaptive one-factor-at-a-time experiments outperform fractional factorial experiments in improving the performance of mechanical engineering systems. Five case studies are presented, each based on data from previously published full factorial physical experiments at two levels. Computer simulations of adaptive one-factor-at-a-time and fractional factorial experiments were carried out with varying degrees of pseudo-random error. For each of the five case studies, the average outcomes are plotted for both approaches as a function of the strength of the pseudo-random error. The main effects and interactions of the experimental factors in each system are presented and analyzed to illustrate how the observed simulation results arise. The case studies show that, for certain arrangements of main effects and interactions, adaptive one-factor-at-a-time experiments exploit interactions with high probability despite the fact that these designs lack the resolution to estimate interactions. Generalizing from the case studies, four mechanisms are described and the conditions are stipulated under which these mechanisms act.

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Copyright © 2006 by American Society of Mechanical Engineers
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Figures

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Figure 1

The proposed adaptive variant of one-factor-at-a-time experimentation as applied to a system with three factors at two levels per factor

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Figure 2

Results for mean life of incandescent bulbs

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Figure 3

Results for voltage of carbon electrodes

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Figure 4

Results for reducing drag torque of a wet clutch pack

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Figure 5

Results for transverse stiffness of glass fiber composites

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Figure 6

Results for standard deviation of bulb life

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Figure 7

A mechanism observed leading adaptive OFAT to exploit a main effect with increased probability given synergistic two-factor interaction

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Figure 8

A mechanism observed leading adaptive OFAT to exploit an anti-synergistic interaction and at least one of two main effects under all starting points and orders in which factors are varied

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Figure 9

A mechanism leading adaptive OFAT to exploit the largest main effect in all cases and also exploit an anti-synergistic interaction under 3 out of 4 of all starting points and orders in which factors are varied

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