July  2007, 3(3): 489-501. doi: 10.3934/jimo.2007.3.489

Optimization of composition and processing parameters for alloy development: a statistical model-based approach

1. 

Department of Industrial and Systems Engineering, 303 Weil Hall, P.O. Box 116595, Gainesville, FL 32611-6595, United States, United States, United States

2. 

Institute for Defense Analysis, 4850 Mark Center Drive, Alexandria, VA 22311-1882, United States

3. 

Department of Statistics, 102 Griffin-Floyd Hall, P.O. Box 118545, Gainesville, FL 32611-8545, United States

Received  March 2005 Revised  January 2007 Published  July 2007

We describe the second step in a two-step approach for the development of new and improved alloys. The first step, proposed by Golodnikov et al [3], entails using experimental data to statistically model tensile yield strength and the 20th percentile of the impact toughness, as a function of alloy composition and processing variables. We demonstrate how the models can be used in the second step to search for combinations of the variables in small neighborhoods of the data space, that result in alloys having optimal levels of the properties modeled. The optimization is performed via the efficient frontier methodology. Such an approach, based on validated statistical models, can lead to a substantial reduction in the experimental effort and cost associated with alloy development. The procedure can also be used at various stages of the experimental program, to indicate what changes should be made in the composition and processing variables in order to shift the alloy development process toward the efficient frontier. Data from these more refined experiments can then be used to adjust the model and improve the second step, in an iterative search for superior alloys.
Citation: Alexandr Golodnikov, Stan Uryasev, Grigoriy Zrazhevsky, Yevgeny Macheret, A. Alexandre Trindade. Optimization of composition and processing parameters for alloy development: a statistical model-based approach. Journal of Industrial & Management Optimization, 2007, 3 (3) : 489-501. doi: 10.3934/jimo.2007.3.489
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