Quality monitoring solution: measurement and modeling of product external diameter in CNC turning

CNC machining is widely used in the manufacturing industry and can produce high-quality products in large quantities. However, defective products occur due to uncertain factors during machining, and monitoring the process to detect and control them early is essential. Furthermore, quality is an important monitoring indicator that can identify factors such as the condition of the process and the timing of tool replacement. Therefore, monitoring studies of CNC machining should consider quality as the monitoring indicator. In this study, we built a product manufacturing process using a CNC turning and acquired process signals and quality data through designed machining experiments. Based on the experimental data, a machine learning algorithm builds various models to identify quality-varying process signals. In addition, we develop a monitoring indicator that represents product quality using the identified signals. This research extracts the features that affect the quality in the frequency domain and validates the features. Also, it can contribute to monitoring technology in the manufacturing field based on CNC machines.