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Online monitoring of tool wear is an indispensable part of industrial production. It allows for real-time assessment of tool condition, enabling tool replacement at the optimal time. This not only reduces the time costs associated with downtime for inspections but also effectively prevents the deterioration of workpiece quality due to excessive wear. Therefore, online monitoring of tool wear is essential for ensuring machining quality and efficiency. This research presents a monitoring technique for tool wear while performing micro-milling by extracting features from machining signals. Initially, the wear mechanism, which includes initial, regular, and severe wear, is analyzed. Tool wear labels are then constructed. Subsequently, the recursive feature elimination (RFE) algorithm optimizes the original feature set from the processed signal, resulting in a subset of relevant features that accurately describe the tool wear. The optimal subset is utilized as an input vector for extreme gradient boosting (XGBoost) training. Next, the best hyperparameter combination for the monitoring model is determined using Bayesian optimization (BO), ensuring the generalization performance of the monitoring model. Finally, the two-edge wear monitoring model is established based on the RFE-BO-XGBoost. To validate the superiority of the RFE-BO-XGBoost model, the performance of the XGBoost model with default parameters in tool wear monitoring is also investigated. The results demonstrate that the constructed monitoring model can effectively identify the tool wear stage. More notably, it precisely predicts the wear status of two cutting edges simultaneously, enabling qualitative and quantitative monitoring of tool wear under normal manufacturing settings. |
