Quality prediction for aluminum diecasting process based on shallow neural network and data feature selection technique

Abstract

This paper aims to develop a prediction algorithm for better product quality of the diecasting process, one of the most essential aspects of quality management in traditional manufacturing industries. Shallow neural network (SNN) architecture has been applied as a regressive classifier engine in order to predict product quality of the manufacturing process to renovate the conventional technology adopted by the mainstream fundamental manufacturing industries. The results obtained have been contrasted with classical multilayer perceptron to establish the efficacy of the proposed strategy. In addition to the predictive classifier, various aspects of the research on the prediction algorithm and optimal input selection for successful yield have also been investigated for the aforementioned process using Fuzzy C Means clustering approach. As a case study for validation, the proposed algorithm applies for data set from an aluminum die-casting process. The results reflect that the proposed analytic model can prove to be an effective tool for the prediction and optimization of the product quality of various fundamental manufacturing processes. (C) 2021 CIRP.