机械加工中的刀具磨损监测与寿命预测
摘 要
机械加工过程中，刀具磨损是影响加工质量和生产效率的关键因素之一。随着智能制造技术的发展，对刀具磨损状态的实时监测与寿命预测已成为提升加工过程稳定性和经济性的核心需求。本研究旨在通过融合多源传感器数据和先进算法模型，构建一种高效、精准的刀具磨损监测与寿命预测方法。具体而言，研究基于振动信号、声发射信号及电流信号等多模态数据，采用小波包分解与主成分分析提取特征，并结合长短期记忆网络（LSTM）建立刀具磨损状态预测模型。此外，为提高预测精度与泛化能力，提出了一种基于迁移学习的优化策略，以适应不同工况下的刀具磨损特性。实验结果表明，该方法能够准确捕捉刀具磨损的动态变化趋势，预测误差低于5%，显著优于传统方法。同时，所提出的迁移学习框架有效降低了因工件材料或切削参数变化带来的预测偏差。
关键词：刀具磨损监测 多源数据融合 长短期记忆网络（LSTM）
Abstract
During machining, tool wear is one of the key factors affecting machining quality and production efficiency. With the development of intelligent manufacturing technology, the real-time monitoring and life prediction of the tool wear state has become the core demand to improve the stability and economy of the processing process. This study aims to construct an efficient and accurate tool wear monitoring and life prediction method by integrating multi-source sensor data and advanced algorithm model. Specifically, based on the multimodal data of vibration signal, acoustic emission signal and current signal, wavelet packet decomposition and principal component analysis are used to extract features, and combined with long and short-term memory network (LSTM). Moreover, to improve the prediction accuracy and generalization ability, an optimization strategy is proposed to adapt the tool wear characteristics under different working conditions. Experimental results show that the proposed method can accurately capture the dynamic trend of tool wear, with a prediction error lower than 5% and significantly better than conventional methods. At the same time, the proposed transfer learning fr amework effectively reduces the prediction bias caused by the variation of workpiece material or cutting parameters.
Keyword:Tool Wear Monitoring Multi-Source Data Fusion Long Short-Term Memory Network (LSTM)
目 录
1绪论 1
1.1机械加工中刀具磨损的研究背景 1
1.2刀具磨损监测与寿命预测的意义 1
1.3国内外研究现状分析 1
1.4本文研究方法概述 2
2刀具磨损监测技术分析 2
2.1刀具磨损的类型与特征 2
2.2常用监测方法及其原理 3
2.3在线监测技术的发展现状 3
2.4监测数据采集与处理方法 4
2.5监测技术的挑战与改进方向 4
3刀具寿命预测模型构建 4
3.1刀具寿命预测的基本理论 4
3.2数据驱动的预测模型设计 5
3.3基于机器学习的寿命预测方法 5
3.4模型验证与误差分析 6
3.5预测模型的实际应用案例 6
4刀具磨损监测与寿命预测系统实现 7
4.1系统架构设计与功能模块划分 7
4.2数据采集与传输方案优化 7
4.3监测与预测算法的集成实现 8
4.4实验验证与结果分析 8
4.5系统性能评估与改进建议 9
结论 9
参考文献 10
致谢 11
摘 要
机械加工过程中，刀具磨损是影响加工质量和生产效率的关键因素之一。随着智能制造技术的发展，对刀具磨损状态的实时监测与寿命预测已成为提升加工过程稳定性和经济性的核心需求。本研究旨在通过融合多源传感器数据和先进算法模型，构建一种高效、精准的刀具磨损监测与寿命预测方法。具体而言，研究基于振动信号、声发射信号及电流信号等多模态数据，采用小波包分解与主成分分析提取特征，并结合长短期记忆网络（LSTM）建立刀具磨损状态预测模型。此外，为提高预测精度与泛化能力，提出了一种基于迁移学习的优化策略，以适应不同工况下的刀具磨损特性。实验结果表明，该方法能够准确捕捉刀具磨损的动态变化趋势，预测误差低于5%，显著优于传统方法。同时，所提出的迁移学习框架有效降低了因工件材料或切削参数变化带来的预测偏差。
关键词：刀具磨损监测 多源数据融合 长短期记忆网络（LSTM）
Abstract
During machining, tool wear is one of the key factors affecting machining quality and production efficiency. With the development of intelligent manufacturing technology, the real-time monitoring and life prediction of the tool wear state has become the core demand to improve the stability and economy of the processing process. This study aims to construct an efficient and accurate tool wear monitoring and life prediction method by integrating multi-source sensor data and advanced algorithm model. Specifically, based on the multimodal data of vibration signal, acoustic emission signal and current signal, wavelet packet decomposition and principal component analysis are used to extract features, and combined with long and short-term memory network (LSTM). Moreover, to improve the prediction accuracy and generalization ability, an optimization strategy is proposed to adapt the tool wear characteristics under different working conditions. Experimental results show that the proposed method can accurately capture the dynamic trend of tool wear, with a prediction error lower than 5% and significantly better than conventional methods. At the same time, the proposed transfer learning fr amework effectively reduces the prediction bias caused by the variation of workpiece material or cutting parameters.
Keyword:Tool Wear Monitoring Multi-Source Data Fusion Long Short-Term Memory Network (LSTM)
目 录
1绪论 1
1.1机械加工中刀具磨损的研究背景 1
1.2刀具磨损监测与寿命预测的意义 1
1.3国内外研究现状分析 1
1.4本文研究方法概述 2
2刀具磨损监测技术分析 2
2.1刀具磨损的类型与特征 2
2.2常用监测方法及其原理 3
2.3在线监测技术的发展现状 3
2.4监测数据采集与处理方法 4
2.5监测技术的挑战与改进方向 4
3刀具寿命预测模型构建 4
3.1刀具寿命预测的基本理论 4
3.2数据驱动的预测模型设计 5
3.3基于机器学习的寿命预测方法 5
3.4模型验证与误差分析 6
3.5预测模型的实际应用案例 6
4刀具磨损监测与寿命预测系统实现 7
4.1系统架构设计与功能模块划分 7
4.2数据采集与传输方案优化 7
4.3监测与预测算法的集成实现 8
4.4实验验证与结果分析 8
4.5系统性能评估与改进建议 9
结论 9
参考文献 10
致谢 11
