Track: Modeling and Simulation

Abstract

Defect reduction has always been the continuous improvement topic that is being addressed in the manufacturing industry. Even nowadays, that the world is moving into the industrial 4.0, such a particular topic still has never outdated, only the new approaches have been introduced for the better achievement of defect reduction. This research aims to reduce the defects in die-casting process of the Hard Disk Drive (HDD) component manufacturing company, focusing on the effects of various machine parameters on the defects occurring in casting products. Predictive maintenance approach and machine learning have been introduced to determine the suitable data modelling technique. 
•    The most related independent factors can be identified through Feature Importance method.
•    Decision Tree (DT) performed the best results among other classification methods. 
•    The 91.18% accuracy can be obtained by decision tree algorithm. 
However, the ratio of labelled data still needs to be reviewed and optimized for the future work as well as continue the actual checking on the frontline production results with the Subject-Matter Expert (SME) also required in order to obtain the best prediction results.