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Intelligent Experimental Design Using an Artificial Neural Network Meta Model and Information Theory

Ability to rapidly design products and their manufacturing process is a key to being competitive in a dynamic market environment. Traditional methods of design of experiment development are unsatisfactory when applied to design problems with large number of input variables and nonlinear input-output relation. A meta-model driven experimental design scheme is developed. The approach uses artificial neural network as the meta-model, and a combination of random-search, fuzzy classification, and information theory as the design tool. An information free energy index is developed which balances the needs for resolving the uncertainty of the model and the relevance to finding the optimal design. The procedure involves iterative steps of meta-model construction, designing new experiments using meta-model and actual execution of designed experiments. The effectiveness of this approach is benchmarked using a simple optimization problem. Three industrial examples are presented to illustrate its applicability to a variety of design problem.