摘 要
超精密加工技术作为现代制造业的核心领域之一，在航空航天、光学器件和医疗器械等领域具有重要应用价值，而刀具磨损是影响加工精度和表面质量的关键因素。为解决传统监测方法难以实时准确反映刀具状态的问题，本研究旨在开发一种基于多源信号融合的刀具磨损监测与控制方法。通过结合切削力、振动信号及声发射信号等多维数据，提出了一种基于深度学习的刀具磨损状态识别模型，并引入自适应控制策略以动态调整切削参数，从而实现对刀具磨损的有效抑制。实验结果表明，所提出的监测方法能够以高于95%的准确率识别刀具的不同磨损阶段，同时自适应控制策略可显著延长刀具使用寿命达30%以上。此外，本研究创新性地将边缘计算技术应用于实时数据处理，大幅提升了系统的响应速度和稳定性。该研究成果不仅为超精密加工中的刀具磨损问题提供了新的解决方案，还为智能制造领域的数字化监控与优化奠定了理论和技术基础，具有重要的学术意义和工程应用价值。
关键词：超精密加工；刀具磨损监测；多源信号融合
Abstract: Ultr machining technology, as one of the core areas of modern manufacturing, plays a significant role in fields such as aerospace, optical devices, and medical instruments, where it exhibits substantial application value. Tool wear remains a critical factor influencing machining accuracy and surface quality. To address the limitations of traditional monitoring methods, which struggle to reflect tool conditions in real-time with sufficient precision, this study aims to develop a tool wear monitoring and control approach based on multi-source signal fusion. By integrating multidimensional data, including cutting forces, vibration signals, and acoustic emission signals, a deep learning-based model for identifying tool wear states is proposed. Additionally, an adaptive control strategy is introduced to dynamically adjust cutting parameters, thereby effectively mitigating tool wear. Experimental results demonstrate that the proposed monitoring method can identify different stages of tool wear with an accuracy exceeding 95%, while the adaptive control strategy significantly extends tool life by more than 30%. Furthermore, this research innovatively applies edge computing technology to real-time data processing, markedly enhancing system responsiveness and stability. The findings not only provide a novel solution to tool wear issues in ultr machining but also lay a theoretical and technical foundation for digital monitoring and optimization in the realm of smart manufacturing, holding considerable academic significance and engineering application value.
Keywords: Ultra-Precision Machining; Tool Wear Monitoring; Multi-Source Signal Fusion
目 录
1绪论 1
1.1超精密加工与刀具磨损的研究背景 1
1.2刀具磨损监测与控制的意义分析 1
1.3国内外研究现状综述 1
1.4本文研究方法与技术路线 2
2刀具磨损的机理与影响因素分析 2
2.1超精密加工中刀具磨损的基本类型 2
2.2材料特性对刀具磨损的影响 3
2.3加工参数与刀具磨损的关系研究 3
2.4环境因素对刀具磨损的作用机制 4
2.5刀具材料选择与优化策略 4
3刀具磨损监测技术的研究 5
3.1基于传感器的刀具磨损监测方法 5
3.2在线监测系统的构建与实现 5
3.3数据采集与信号处理技术应用 6
3.4监测精度提升的关键技术探讨 6
3.5实时监测中的误差分析与校正 7
4刀具磨损控制策略与优化方法 7
4.1刀具磨损预测模型的建立与验证 7
4.2基于人工智能的刀具磨损控制算法 8
4.3加工工艺参数的优化设计 8
4.4刀具寿命管理与延长策略研究 9
4.5控制策略的实际应用案例分析 9
结论 11
参考文献 12
致 谢 13
