Semi-supervised Prediction of Milling Cutter Wear Based on an Empirical Formula for Cutting Force and Wear

Abstract

Accurately predicting tool wear is essential for maintaining high machining quality. Currently, deep learning models are extensively utilized in predicting tool wear. However, deep learning models are prone to local optima, and limited tool wear sample data and multi-sensor feature fusion limit the model's ability to generalize and extract valid information. To address the above problems, this paper proposes a semi-supervised milling cutter wear prediction method based on an empirical formula for cutting force wear, which is modelled (IRM-CFAM) by the Inception-ResNet Module (IRM) and the Channel Feature Adaptive Module (CFAM). By introducing an empirical formula for cutting force wear, the cutting forces in the unlabeled samples are input to estimate wear values and construct wear curves. Based on the constructed wear curves, a monotonic loss function guided by an empirical curve of milling cutter wear is proposed. Meanwhile, the estimated wear values are combined with unlabeled samples to form a second data source, which is then merged with the labeled samples to expand the dataset. The constructed CFAM consists of channel attention and cross-attention mechanisms, which achieves adaptive fusion of weights. Following validation on the PHM2010 milling cutter dataset, the proposed method attained average RMSE and MAE values of 6.693 and 5.136, respectively, across three test sets, showing a significant advantage over other methods. This research facilitates accurate prediction of milling cutter wear and introduces a novel approach for expanding sample data.