PINN-Based Tool Wear Modeling and Prediction

Zhang Yanping; Kho Lee Chin; Wang Zhihan; Zhang Mingqiang and Yuan Dongfeng

doi:https://doi.org/10.14445/22315381/IJETT-V73I11P105

Research Article | Open Access | Download PDF

Volume 73 | Issue 11 | Year 2025 | Article Id. IJETT-V73I11P105 | DOI : https://doi.org/10.14445/22315381/IJETT-V73I11P105

PINN-Based Tool Wear Modeling and Prediction

Zhang Yanping, Kho Lee Chin, Wang Zhihan, Zhang Mingqiang and Yuan Dongfeng

Received	Revised	Accepted	Published
24 Jul 2025	24 Oct 2025	03 Nov 2025	25 Nov 2025

Citation :

Zhang Yanping, Kho Lee Chin, Wang Zhihan, Zhang Mingqiang and Yuan Dongfeng, "PINN-Based Tool Wear Modeling and Prediction," International Journal of Engineering Trends and Technology (IJETT), vol. 73, no. 11, pp. 51-63, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I11P105

Abstract

To address the challenges of low prediction accuracy and weak physical interpretability in tool wear modeling, this study proposes a Physics-Informed Neural Network (PINN)-based hybrid framework that integrates wear stage perception and physical prior knowledge. Four representative models-Long Short-Term Memory (LSTM), Stepwise Dual-Driven, Basquin-based PINN, and Empirical Formula-PINN (EF-PINN)-are constructed and systematically evaluated using real milling vibration datasets. The EF-PINN embeds empirical wear laws as soft physical constraints within the neural network loss function, enabling a balanced fusion of data-driven adaptability and physical interpretability. Experimental results demonstrate that EF-PINN achieves superior performance in wear trend fitting, nonlinear degradation modeling, and generalization under varying cutting conditions, significantly outperforming traditional data-driven and purely mechanism-based approaches. The main contributions of this work are: (1) Establishing a unified comparative framework for data-, hybrid-, and physics-informed models; (2) Developing an EF-PINN that bridges the gap between empirical knowledge and data-driven learning; and (3) Experimentally validating the effectiveness of integrating physical priors to enhance reliability and confidence. This study provides a new paradigm for high-precision, interpretable, and robust tool wear prediction in intelligent manufacturing.

Keywords

Tool Wear Prediction, PINN, LSTM, Basqui, Intelligent manufancturing.

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