Application of taguchi-based neural network prediction design of tempcore process

In this paper, an approach for developing the prediction model for Tempcore process using a back-propagation neural network (BPNN) combined with the Taguchi's parameter design is presented. Here, an attempt has been made to improve the deficiencies in current neural networks associated with the design of network architecture, including the selection of one optimal set of learning parameters to accomplish faster convergence during training and the desired accuracy during the recall step. The objective of the prediction model is to explore the relationships between the control factor levels and the yield strength for steel bars produced by Tempcore process. In addition, the feasibility of adopting this approach to forecast the target characteristics of the process with various control conditions in the manufacturing system has been demonstrated.