Abstract

Today’s manufacturing methods are caught between the growing need for quality, high process safety, minimal manufacturing costs, and short manufacturing times. In order to meet these demands, process setting parameters have to be selected carefully with close attention being paid to quality needs. For such optimization it is necessary to represent the processes in a model. Due to the enormous complexity of many processes and the high number of influencing parameters, however, conventional approaches to modeling and optimization are no longer sufficient. This article shows how the application of neural networks for process modeling can simplify these highly complex interdependences so that both process and quality parameters can be assessed before or during processing. By connecting them with corresponding cost models, it is possible to optimize processes with the help of evolutionary algorithms. Using different manufacturing processes as examples, the authors demonstrate how process modeling can be optimized using neural networks and evolutionary algorithms.

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