A Bayesian Regularization Approach to Predict the Quality of Injection-Moulded Components by statistical SVM for Online Monitoring system

Abstract

To evaluate the quality of injection-molded components, conventional approaches are costly, time-consuming, or based on statistical process control characteristics that are not always accurate. Machine learning might be used to categorise components based on their quality. In order to accurately estimate the quality of injection moulded components, this study uses a SVM classifier. In addition, the form of the spare components after the working method product in simulation is classified as "qualified" or "unqualified". The quality indicators have an excellent association with data recordings from the original database of various sensors such as pressure and temperature used in the proposed network model for online prediction. The outliers are removed from the input original data to minimize the deviation of precision or prediction accuracy of the model performance metrics. Data points in the "to-be-confirmed" region (which is in the fit line area) may be misjudged by this statistical SVM model since it is placed between the "qualified" and "unqualified" areas. This statistical procedure in the proposed SVM model also uses Bayesian regularisation to classify final components into distinct quality levels.