Duan Jingsong, Cao Guohua, Ma Guoqing, Yu Zhenglin, Shao Changshun
School of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, China.
Changchun University of Science and Technology Chongqing Research Institute, Chongqing 401133, China.
Sensors (Basel). 2024 Sep 11;24(18):5888. doi: 10.3390/s24185888.
The state of a grinding wheel directly affects the surface quality of the workpiece. The monitoring of grinding wheel wear state can allow one to efficiently identify grinding wheel wear information and to timely and effectively trim the grinding wheel. At present, on-line monitoring technology using specific sensor signals can detect abnormal grinding wheel wear in a timely manner. However, due to the non-linearity and complexity of the grinding wheel wear process, as well as the interference and noise of the sensor signal, the accuracy and reliability of on-line monitoring technology still need to be improved. In this paper, an intelligent monitoring system based on multi-sensor fusion is established, and this system can be used for precise grinding wheel wear monitoring. The proposed system focuses on titanium alloy, a typical difficult-to-process aerospace material, and addresses the issue of low on-line monitoring accuracy found in traditional single-sensor systems. Additionally, a multi-eigenvalue fusion algorithm based on an improved support vector machine (SVM) is proposed. In this study, the mean square value of the wavelet packet decomposition coefficient of the acoustic emission signal, the grinding force ratio of the force signal, and the effective value of the vibration signal were taken as inputs for the improved support vector machine, and the recognition strategy was adjusted using the entropy weight evaluation method. A high-precision grinding machine was used to carry out multiple sets of grinding wheel wear experiments. After being processed by the multi-sensor integrated precision grinding wheel wear intelligent monitoring system, the collected signals can accurately reflect the grinding wheel wear state, and the monitoring accuracy can reach more than 92%.
砂轮的状态直接影响工件的表面质量。对砂轮磨损状态进行监测能够使人有效地识别砂轮磨损信息，并及时、有效地修整砂轮。目前，利用特定传感器信号的在线监测技术能够及时检测出砂轮的异常磨损。然而，由于砂轮磨损过程的非线性和复杂性，以及传感器信号的干扰和噪声，在线监测技术的准确性和可靠性仍有待提高。本文建立了一种基于多传感器融合的智能监测系统，该系统可用于精确的砂轮磨损监测。所提出的系统聚焦于钛合金这种典型的难加工航空航天材料，并解决了传统单传感器系统中在线监测精度低的问题。此外，还提出了一种基于改进支持向量机（SVM）的多特征值融合算法。在本研究中，将声发射信号的小波包分解系数的均方值、力信号的磨削力比以及振动信号的有效值作为改进支持向量机的输入，并采用熵权评价法调整识别策略。使用高精度磨床进行了多组砂轮磨损实验。经多传感器集成精密砂轮磨损智能监测系统处理后，采集到的信号能够准确反映砂轮的磨损状态，监测精度可达92%以上。
