Online Tool Wear Prediction Under Variable Cutting Conditions

Tool wear prediction is crucial for maintaining machining quality and improving production efficiency. Adapting to variable cutting conditions in a dynamic manufacturing environment remains a significant research topic. Existing methods for tool wear prediction under variable cutting conditions require retraining with labeled samples collected from new cutting conditions, necessitating trial cutting experiments to collect labeled samples and the storage of a separate model for each new cutting condition. This results in high manual labeling costs and substantial storage requirements, and prevents models trained on known conditions from being directly or efficiently deployed to new cutting conditions. To overcome these limitations, we propose a novel setting for tool wear prediction under variable cutting conditions. In this setting, the prediction model is not exposed to any data from the new cutting conditions during training and must be directly deployed to these new cutting conditions after training. To address this challenge, we propose an online, real-time quantitative tool wear prediction method under variable cutting conditions. The proposed method employs a domain generalization (DG) algorithm that incorporates classwise gradient consistency (CGC) and domain-invariant feature-level sample mixing to train the prediction model. This strategy enables the prediction model trained on known cutting conditions to be directly deployed to new, unknown cutting conditions without retraining, thus allowing online quantitative tool wear prediction under new cutting conditions. Experimental results indicate that the variable cutting conditions can degrade the performance of traditional data-driven methods and models. The proposed method reduces the mean absolute error (MAE) by at least 33.02% compared to traditional data-driven methods and by at least 10.03% compared to the state-of-the-art DG algorithms.