(370q) Reverse Mapping of Nonlinear Process Responses By a Trained Neural Network

This paper presents a novel and a very effective technique based on the artificial neural net (ANN) technology to reverse map from process response to the input (control) variables when the input-response relationships are nonlinear, complex or intractable by theory. This is often the problem when the control variables in manufacturing or prototype development must be set such that the response hits a specified target. This present approach avoids countless empirical searches in the decision space and thus minimizes the expenditure on R&D or production resources. Additionally, this paper illustrates how one may build an ANN model with top flight performance in Excel® when a commercial neural net software is unavailable. The paper includes the learning oriented reworking of a well-established example from the response surface literature.