In many manufacturing cases, engineers are required to optimize a number of responses simultaneously. A common approach for the optimization of multiple-response problems begins with using polynomial regression models to estimate the relationships between responses and control factors. Then, a technique for combining different response functions into a single scalar, such as a desirability function, is employed and, finally, an optimization method is used to find the best settings for the control factors. However, in certain cases, relationships between responses and control factors are far too complex to be efficiently estimated by polynomial regression models. In addition, in many manufacturing cases, engineers encounter qualitative responses, which cannot be easily stated in the form of numbers. An alternative approach proposed in this paper is to use an artificial neural network (ANN) to estimate the quantitative and qualitative response functions. In the optimization phase, a genetic algorithm (GA) is considered in conjunction with an unconstrained desirability function to determine the optimal settings for the control factors. Two manufacturing examples in which engineers were asked to optimize multiple responses from the semiconductor and textile industries are included in this article. The results indicate the strength of the proposed approach in the optimization of multiple-response problems.
Design of experiment Response surface methodology Artificial neural networks Genetic algorithm, multiple layer perceptron Radial basis function Mean square error