A Decision Tree Approach for Identifying Defective Products in the Manufacturing Process

Recently, due to the significance of Industry 4.0, the manufacturing industry is developing globally. Conventionally, the manufacturing industry generates a large volume of data that is often related to process, line and products. In this paper, we analyzed causes of defective products in the manufacturing process using the decision tree technique, that is a well-known technique used in data mining. We used data collected from the domestic manufacturing industry that includes Manufacturing Execution System (MES), Point of Production (POP), equipment data accumulated directly in equipment, in-process/external air-conditioning sensors and static electricity. We propose to implement a model using C4.5 decision tree algorithm. Specifically, the proposed decision tree model is modeled based on components of a specific part. We propose to identify the state of products, where the defect occurred and compare it with the generated decision tree model to determine the cause of the defect.