Catastrophe Theory-Informed Hybrid Modeling for Chip Splitting State and Specific Cutting Force Prediction in Thread Turning

This is the authors' abstract. We don't add key points for this paper.

Chip-splitting can reduce cutting resistance, but its abrupt transition makes quantitative prediction difficult. This study proposes a catastrophe theory-informed framework for predicting the chip state and specific cutting force in the thread turning of 6061-T6 aluminum alloy. The framework combines a random forest classifier with a fully connected neural network and uses domain-adversarial training to introduce the fold geometry of a cusp catastrophe equilibrium surface as a regularizer. The experimental dataset comprises 1252 densely sampled equilibrium points from seven cutting force curves. Classification used a stratified 1001/251 train/test split, whereas regression held out Experiments 2, 4, and 6 (539 samples) and used the remaining 713 samples for training and validation. The random forest achieved 100.00% accuracy on the stratified test set. Across three initializations, the full regression model achieved MAPE = 1.74% ± 0.36%, R2 = 0.9815 ± 0.0020, maximum absolute percentage error = 4.47%, and normalized RMSE = 0.1315, which outperformed transfer learning, support vector regression, and the unconstrained FCN. The proposed framework shows that combining catastrophe theory geometry with machine learning can improve the prediction of chip-splitting states and specific cutting force in thread turning.