An Adaptive One-Factor-at-a-Time Method for Robust Parameter Design: Comparison With Crossed Arrays Via Case Studies

2006 ◽  
Author(s):  
Daniel D. Frey ◽  
Nandan Sudarsanam ◽  
Jeffrey B. Persons
Keyword(s):  
Case Studies ◽  
Parameter Design ◽  
Robust Parameter Design ◽  
Robust Parameter ◽  
Crossed Arrays ◽  
Design Comparison

An Adaptive One-Factor-at-a-Time Method for Robust Parameter Design: Comparison With Crossed Arrays via Case Studies

2007 ◽  
Vol 130 (2) ◽  
Author(s):  
Daniel D. Frey ◽  
Nandan Sudarsanam
Keyword(s):  
Case Studies ◽  
Experimental Error ◽  
Fractional Factorial ◽  
Parameter Design ◽  
Robust Parameter Design ◽  
Control Factors ◽  
Starting Point ◽  
Robust Parameter ◽  
Crossed Arrays ◽  
Noise Factors

This paper presents a conceptually simple and resource efficient method for robust parameter design. The proposed method varies control factors according to an adaptive one-factor-at-a-time plan while varying noise factors using a two-level resolution III fractional factorial array. This method is compared with crossed arrays by analyzing a set of four case studies to which both approaches were applied. The proposed method improves system robustness effectively, attaining more than 80% of the potential improvement on average if experimental error is low. This figure improves to about 90% if prior knowledge of the system is used to define a promising starting point for the search. The results vary across the case studies, but, in general, both the average amount of improvement and the consistency of the results are better than those provided by crossed arrays if experimental error is low or if the system contains some large interactions involving two or more control factors. This is true despite the fact that the proposed method generally uses fewer experiments than crossed arrays. The case studies reveal that the proposed method provides these benefits by exploiting, with high probability, both control by noise interactions and also higher order effects involving two control factors and a noise factor. The overall conclusion is that adaptive one-factor-at-a-time, used in concert with factorial outer arrays, is demonstrated to be an effective approach to robust parameter design providing significant practical advantages as compared to commonly used alternatives.

A semi-parametric approach to robust parameter design

2008 ◽  
Vol 138 (1) ◽  
pp. 114-131 ◽  
Author(s):  
Stephanie M. Pickle ◽  
Timothy J. Robinson ◽  
Jeffrey B. Birch ◽  
Christine M. Anderson-Cook
Keyword(s):  
Parameter Design ◽  
Robust Parameter Design ◽  
Parametric Approach ◽  
Robust Parameter

scholarly journals A Confidence Region for Zero-Gradient Solutions for Robust Parameter Design Experiments

2011 ◽  
Vol 2011 ◽  
pp. 1-11
Author(s):  
Aili Cheng ◽  
John Peterson ◽  
Pallavi Chitturi
Keyword(s):  
Confidence Region ◽  
Control Variable ◽  
Critical Values ◽  
Parameter Design ◽  
Feasible Region ◽  
Robust Parameter Design ◽  
Control Variables ◽  
Controllable Factors ◽  
Key Issues ◽  
Robust Parameter

One of the key issues in robust parameter design is to configure the controllable factors to minimize the variance due to noise variables. However, it can sometimes happen that the number of control variables is greater than the number of noise variables. When this occurs, two important situations arise. One is that the variance due to noise variables can be brought down to zero The second is that multiple optimal control variable settings become available to the experimenter. A simultaneous confidence region for such a locus of points not only provides a region of uncertainty about such a solution, but also provides a statistical test of whether or not such points lie within the region of experimentation or a feasible region of operation. However, this situation requires a confidence region for the multiple-solution factor levels that provides proper simultaneous coverage. This requirement has not been previously recognized in the literature. In the case where the number of control variables is greater than the number of noise variables, we show how to construct critical values needed to maintain the simultaneous coverage rate. Two examples are provided as a demonstration of the practical need to adjust the critical values for simultaneous coverage.

Sign in / Sign up

Export Citation Format

Share Document