Support Vector Machines and Random Forest Classification Models for Identification of Stability in Extrusion Film Casting Process

Abstract

Extrusion film casting (EFC) is a very important industrial process employed for producing large amounts of thin films for multiple uses. It is important to identify input process parameters for maintaining stable and continuous trouble-free operation. A priori modelling and solution of governing phenomenological equations to achieve this requires tedious computational efforts. As an alternative in this work we have employed Machine learning to build data driven EFC classification models to predict stability of any given set of operating conditions. We have used accurate input data for certain combinations of parameters along with their respective output classes to build robust classification models. We used Support Vector Machines (SVM) and Random Forest classifiers for this purpose. We created four different data sets with different process parameter combinations and number of relaxation modes. We also used the Synthetic Minority Oversampling Technique (SMOTE) to handle data imbalance. Our simulation results indicate that prediction of stability/instability classes for different process parameters can be achieved with high degree of confidence with robust machine learning models.