Hybrid Machine Learning for CNC Process Monitoring

The transition to highly customized, one-off production in modern manufacturing necessitates sophisticated process monitoring to reduce waste, minimise downtime, and alleviate operator burden. Computer Numerically Controlled (CNC) axes represent a fundamental component of automated manufacturing and offer a universal and accessible monitoring option through power supply data. By accurately predicting reference signals and comparing them with real-time measurements, deviations can be used for effective model-based process monitoring and anomaly detection. This study explores the efficacy of hybrid machine learning (ML) models in predicting reference signals for CNC axes using features derived from a physical model. Additionally, relevant but difficult-to-measure features such as process forces and the material removal rate (MMR) were made accessible through soft sensors. Various ML models were evaluated, including tree-based models (e.g. random forest (RF) and gradient boosting (GB)) and deep learning (DL) models (e.g. feed-forward neural networks (FNN), long short-term memory (LSTM), and transformers-based models (TF)). Feature importance analysis was performed, identifying velocity, acceleration, process forces, spindle torque, andMMRas crucial predictors that influence model performance. Key results indicate that tree-based models, specifically RF and GB, consistently delivered the highest accuracy, achieving R2 up to 0.98 for translatory axes and approximately 0.89 for the main spindle. These models demonstrated robustness, outperforming deep learning approaches, particularly when trained on smaller datasets. Although DL models improved with larger data volumes, their performance remained inferior compared to tree-based methods. The study underscores the potential of the integration of physical knowledge into hybrid ML models to enhance model-based process monitoring.