An analysis of an optical coating process capability prediction model in a Six Sigma procedure by integrating APIBPN and K-means

This study focused on plastic panel products, and treated product penetration rate as a quality characteristic to construct an optical coating process capability prediction model. This study applied Six Sigma to conduct an empirical study on an optical film processing plant.Combining the Taguchi’s parameter design method and the back propagation neural network (BPNN) prediction method, this study used the orthogonal array in experimental design, and employed the Taguchi method to analyse the data obtained from the orthogonal array experiments to study the key factors and their levels, in order to determine the optimal process parameter combinations. The influential process parameters were input into the after ‘apicalis’ in Pachycondyla apicalis optimise back propagation network (APIBPN) and K-means, the outputs of which were the prediction results of the coating film process capability. The results can provide engineers a reference in quality improvement and decision-making planning. The experimental results suggested that the Cpk was improved from 0.89 to 1.63, indicating a significant improvement in process capability value. The prediction accuracy rate is over 97.6%, which is better than that of the trial and error method, and can improve coating film process capabilities and product quality.