Health monitoring of CNC machining processes using machine learning and wavelet packet transform

Abstract

Monitoring the health of a machining process plays a crucial role in advancing fully automated manufacturing. Faulty machining processes (abnormal process) leads to part rejection and can damage the CNC machines. In this context, data-driven machine learning (ML) models with wavelet packet transform (WPT)-based feature extraction method have gained significant importance in health monitoring. The study of different ML models, mother wavelets, and decomposition levels on real production data is important for effective process health monitoring but are not explored well in the literature. This study analyzes the performance of fifty mother wavelets, two decomposition levels (L2 and L3), and five ML models using actual production facility CNC machine dataset to achieve accurate process health monitoring. Various performance indicators obtained from the confusion matrix have been used to evaluate the ML models. The findings indicate that the Random Forest (RF) and Light-Gradient boosting machine (GBM), using the coif8 and db14 mother wavelet, at decomposition level (L3), deliver the best classification performance. Furthermore, the RF model consistently achieves good precision, sensitivity, accuracy, and F1 scores across most classes, making it a reliable and efficient option for the classification of normal and abnormal machining processes. The SVC, MLP, and CNN models, however, also exhibit competitive performance.