Lin Zhangwen, Fan Yankun, Tan Jinling, Li Zhen, Yang Peng, Wang Hua, Duan Weiwei
College of Mechanical Engineering, Anhui Institute of Information Technology, Wuhu, 241199, Anhui, China.
College of Mechanical and Power Engineering, China Three Gorges University, Yichang, 443002, China.
Sci Rep. 2025 Jan 24;15(1):3096. doi: 10.1038/s41598-025-85694-9.
To address the challenge of accurately capturing tool wear states in small sample scenarios, this paper proposes a tool wear prediction method that combines XGBoost feature selection with a PSO-BP network. In order to solve the problem of input feature selection and parameter selection in BP neural network, a double-layer programming model of input feature and parameter selection is established, which is solved by XGBoost and PSO. Initially, vibration and cutting force signals from CNC machining are preprocessed using time-domain segmentation, Hampel filtering, and wavelet denoising. Subsequently, time-domain, frequency-domain, and time-frequency domain features are extracted from the preprocessed data using FFT and wavelet packet decomposition, followed by feature screening for tool wear mapping via Pearson correlation and XGBoost feature importance analysis as model input. Finally, PSO is employed to optimize BPNN parameters. Experimental results show that PSO outperforms other algorithms in training the tool wear prediction model, with XGBoost feature selection reducing model construction time by 57.4% and increasing accuracy by 63.57%, demonstrating superior feature selection capabilities over Decision Tree, Random Fores, Adaboost and Extra Trees. These findings suggest that the proposed method can effectively predict tool wear in real-world CNC machining, contributing to improved production efficiency, reduced tool replacement frequency, and lower maintenance costs, thereby providing valuable insights for industrial applications.
为应对小样本场景下准确捕捉刀具磨损状态的挑战，本文提出了一种将XGBoost特征选择与PSO-BP网络相结合的刀具磨损预测方法。为解决BP神经网络中的输入特征选择和参数选择问题，建立了输入特征和参数选择的双层规划模型，通过XGBoost和PSO求解。首先，利用时域分割、汉佩尔滤波和小波去噪对数控加工中的振动和切削力信号进行预处理。随后，使用快速傅里叶变换（FFT）和小波包分解从预处理数据中提取时域、频域和时频域特征，接着通过皮尔逊相关性和XGBoost特征重要性分析进行特征筛选，以将刀具磨损映射作为模型输入。最后，采用粒子群优化算法（PSO）优化BP神经网络（BPNN）参数。实验结果表明，在训练刀具磨损预测模型方面，PSO优于其他算法，XGBoost特征选择使模型构建时间减少57.4%，准确率提高63.57%，在特征选择能力上优于决策树、随机森林、Adaboost和极端随机树。这些研究结果表明，所提出的方法能够有效地预测实际数控加工中的刀具磨损，有助于提高生产效率、降低刀具更换频率和维护成本，从而为工业应用提供有价值的见解。
