Approaches to identify relevant process variables in injection moulding using beta regression and SVM

In this paper, we analyze data from an injection moulding process to identify key process variables which influence the quality of the production output. The available data from the injection moulding machines provide information about the run-time, setup parameters of the machines and the measurements of different process variables through sensors. Additionally, we have data about the total output produced and the number of scrap parts. In the first step of the analysis, we preprocessed the data by combining the different sets of data for a whole process. Then we extracted different features, which we used as input variables for modeling the scrap rate. For the predictive modeling, we employed three different models, beta regression with the backward selection, beta boosting with regularization and SVM regression with the radial kernel. All these models provide a set of common key features which affect the scrap rates.