Optimization of Insert-Tray Matching using Machine Learning

Abstract: The manufacturing process of carbide inserts at Sandvik Coromant consists of several operations. During some of these, the inserts are positioned on trays. For some inserts the trays are pre-defined but for others the insert-tray matching is partly improvised. The goal of this thesis project is to examine whether machine learning can be used to predict which tray to use for a given insert. It is also investigated which insert features are determining for the choice of tray. The study is done with insert and tray data from four blasting operations and considers a set of standardized inserts since it is assumed that the tray matching for these is well tuned. The algorithm that is used for the predictions is the supervised learning algorithm k-nearest neighbors. The problem of identifying the determining features is regarded as a feature selection problem and is done with the ReliefF algorithm. From the classification results it is seen that the classifiers are overfitting. The main reason for this is probably that the datasets contain features that together are uniquely defining for which tray is used. This was not detected during the feature selection since ReliefF identifies features that are individually relevant to the output. An idea to avoid overfitting the classifiers is to exclude these defining features from the dataset. Further work is thus recommended.