Abstract
The application of the uniform design (UD) method to nonlinear multivariate calibration by an artificial neural network (ANN) can be used to build a model for an unknown process efficiently because it allows many levels for each factor. If the cost of each experiment is high, low partitioned levels are usually proposed first to carry out the experiments. However, if a reliable ANN model cannot be obtained from the designed experiments, the sequential pseudo-uniform design (SPUD) method developed here can be employed to locate additional experiments in the experimental region. An information free energy index is used to validate the identified ANN model. Once the identified model is verified as reliable, the optimal operating conditions can be determined to guide the process to the desired objective. The simulation results demonstrate that the product and process development based on the proposed method require only a reasonable number of experiments.
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