Self learning strategies for experimental design and response surface optimization

Most preset RSM designs offer ease of implementation and good performance over a wide range of process and design optimization applications. These designs often lack the ability to adapt the design based on the characteristics of application and experimental space so as to reduce the number of experiments necessary. Hence, they are not cost effective for applications where the cost of experimentation is high or when the experimentation resources are limited. In this dissertation, we present a number of self-learning strategies for optimization of different types of response surfaces for industrial experiments with noise, high experimentation cost, and requiring high design optimization performance. The proposed approach is a sequential adaptive experimentation approach which combines concepts from nonlinear optimization, non-parametric regression, statistical analysis, and response surface optimization. The proposed strategies uses the information gained from the previous experiments to design the subsequent experiment by simultaneously reducing the region of interest and identifying factor combinations for new experiments. Its major advantage is the experimentation efficiency such that, for a given response target, it identifies the input factor combination (or containing region) in less number of experiments than the classical designs. Through extensive simulated experiments and real-world case studies, we show that the proposed ASRSM method clearly outperforms the classical CCD and BBD methods, works superior to optimal A- D- and V- optimal designs on average and compares favorably with global optimizations methods including Gaussian Process and RBF