Sensor Data Modeling and Model Frequency Analysis for Detecting Cutting Tool Anomalies in Machining
Sensor Data Modeling and Model Frequency Analysis for Detecting Cutting Tool Anomalies in Machining
复制标题
加工过程中刀具异常检测的传感器数据建模与模频分析
DOI：
10.1109/tsmc.2022.3218536
复制
发表时间：
2022-11-10
影响因子：
8.7
通讯作者：
Stammers, Jon
中科院分区：
文献类型：
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作者：
Liu, Zepeng;Lang, Zi-Qiang;Stammers, Jon
先进制造中的刀具状态监测（TCM）涉及切削刀具的运行状态监测和损伤诊断。在本研究中，提出了一种基于传感器数据建模和模型频率分析的创新TCM方法。新方法创造了对传统TCM技术的范式转变，并有可能实现满足先进制造要求的自主切削刀具异常诊断。当应用所提出的方法时，来自传感器的数据不直接用于监测切削刀具状态。相反，来自传感器的数据被用来构建动态过程模型。这样可以提取加工过程的独特频域特性并用于实时显示切削刀具的健康状况。进行实验研究以验证所提出方法的有效性并证明新方法相对于传统中医技术的优越性。
Tool condition monitoring (TCM) in advanced manufacturing is concerned with cutting tool operational status monitoring and damage diagnosis. In the present study, an innovative TCM approach based on sensor data modeling and model frequency analysis is proposed. The new approach creates a paradigmatic shift to the conventional TCM techniques and can potentially realize autonomous cutting tool anomaly diagnosis satisfying the requirement of advanced manufacturing. When applying the proposed approach, the data from sensors are not directly utilized for monitoring cutting tool status. Instead, the data from sensors are utilized to build a dynamic process model. This allows the unique frequency-domain properties of the machining process to be extracted and used to reveal, in real time, cutting tool health conditions. Experimental studies are conducted to verify the effectiveness of the proposed approach and to demonstrate the superiority of the new approach over conventional TCM techniques.