Vibration Signal-Based Tool Condition Monitoring Using Regularized Sensor Data Modeling and Model Frequency Analysis
Vibration Signal-Based Tool Condition Monitoring Using Regularized Sensor Data Modeling and Model Frequency Analysis
复制标题
基于正则化传感器数据建模和模频分析的刀具状态监测
DOI：
10.1109/tim.2023.3343825
复制
发表时间：
2024
影响因子：
5.6
通讯作者：
Zepeng Liu;Zi-Qiang Lang;Yufei Gui;Yun-Peng Zhu;Hatim Laalej;David Curtis
中科院分区：
文献类型：
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作者：
Zepeng Liu;Zi-Qiang Lang;Yufei Gui;Yun-Peng Zhu;Hatim Laalej;David Curtis
在先进制造中，刀具状态监测对保证产品质量和提高生产效率起着至关重要的作用。然而，复杂的加工环境往往限制了传统监测系统的监测精度。在本研究中，利用一种新的基于正则化的传感器数据建模和模型频率分析，提出了一种新的加工过程中中医诊断框架。首次将底层加工过程的物理信息纳入建模过程，设计相关的正则化参数。这确保了在建模过程中可以考虑重要的底层物理，从而提高TCM的性能。这种思想被称为工具状态监测导向的正则化（TCMoR）。从基于tmrr的传感器数据建模中识别出模型后，提取模型的频域属性，以揭示用于TCM目的的底层加工过程的独特和有物理意义的特征。在可变和受控的工具-工件接触条件下，通过大量的原位实验研究验证了所提出的诊断框架的有效性，证明了其优于传统中医方法的优势及其在工业中的潜在应用。
Tool condition monitoring (TCM) plays a vital role in maintaining product quality and improving productivity in advanced manufacturing. However, complex machining environments often limit the monitoring accuracy of conventional monitoring systems. In the present study, a new diagnostic framework is proposed for TCM during machining using a novel regularization-based sensor data modeling and model frequency analysis. For the first time, the physical information of the underlying machining process is incorporated into the modeling procedure for the design of the associated regularization parameter. This ensures that significant underlying physics can be taken into account during the modeling so as to enhance the TCM performance. This idea is referred to as tool condition monitoring-oriented regularization (TCMoR). After a model has been identified from TCMoR-based sensor data modeling, the frequency-domain properties of the model are extracted to reveal unique and physically meaningful features of the underlying machining process for the TCM purpose. The effectiveness of the proposed diagnostic framework is validated by extensive in situ experimental studies under both variable and controlled tool-workpiece engagement conditions, demonstrating its advantages over conventional TCM methods and its potential applications in industry.