目的 降低砂轮磨损行为对工件表面质量的影响,开发出新的特征用于表征磨粒磨损,提升声发射监测砂轮磨损的可靠性。方法 以单颗磨粒为研究对象,开展单颗磨粒磨损试验,记录磨粒的出露高度及磨耗面积的变化,根据磨粒磨损体积对磨损状态进行划分。采集磨粒在不同状态下磨粒划擦碳化硅的声发射信号,利用增强鲸鱼优化变分模态分解（EWOA_VMD）的数据处理方法,对原始声发射信号进行预处理。提取信号时域和频域特征,验证所提出的新的多尺度标准差排列熵（MSDPE）特征的可靠性。结果 磨粒的磨损形式为微破碎、磨耗磨损和宏观破碎,不同形式的磨损导致了磨损体积呈现出先急速上升、又趋于平缓、最后又急速上升的趋势。将EWOA_VMD和鲸鱼优化变分模态分解（WOA_VMD）的去噪效果对比,EWOA_VMD的包络熵为更低的6.868 9,EWOA_VMD的收敛速度更快,并且处理后的信号信噪比更高。所提出的MSDPE特征能够准确捕捉磨粒的磨损行为,尺度因子为4时的MSDPE与磨粒磨损体积的相关性系数为0.92。结论 磨粒磨损可分为初期磨损、稳定磨损和严重磨损3个阶段。EWOA_VMD具有收敛速度快、重构信号质量更高的优点,能有效剔除环境噪声。MSDPE特征对磨粒的磨损行为具有较高的敏感性,与磨粒磨损体积的相关性较高。MSDPE能够准确识别磨粒磨损的3个阶段,该特征可以应用于砂轮磨损的声发射监测过程中。
Abstract
Grinding serves as an important method for high efficient and precise machining of hard and brittle materials such as engineering ceramics, and the wear state of the grinding wheel directly affects the surface quality of machined workpieces. The acoustic emission signal characteristics can effectively represent the wear state of the grinding wheels. With a single abrasive particle as the research object, a new acoustic emission characteristic parameter used to characterize the wear state of the grinding wheel is proposed in this study. First, a single abrasive particle wear experiment is carried out under dry cutting conditions according to the cut-in plane reverse cutting method. The grinding wheel speed is set as 2 000 r/min, the grinding depth as 20 μm, the feed speed of the worktable as 0.02 m/s, and the workpiece was sintered silicon carbide ceramic block under normal pressure. During the experiment, acoustic emission sensors are used to collect the signals generated by the abrasive particle scratching silicon carbide ceramic under different wear conditions, and a Dino AM4115T handheld electron microscope is used to record the evolution of abrasive particle morphology. The initial exposed height of abrasive particle is 0.774 mm, and the initial wear platform area is 0 mm2. The wear volume of the abrasive particle can be calculated by the wear height and wear platform area of the abrasive particle. The wear volume is used to quantify the wear process of the abrasive particle, and its change shows a trend of rapid rise first, then tending to be flat, and finally rapid rise. The wear process can be divided into three stages according to the wear volume of the abrasive particle: initial wear dominated by micro fracture, stable wear dominated by wear and serious wear dominated by macro fracture. Subsequently, an improved Enhanced Whale Optimized Variable Mode Decomposition (EWOA_VMD) method is proposed for the pretreatment of acoustic emission signals. Compared with the traditional Whale Optimized Variable Mode Decomposition (WOA_VMD) method, the entropy of the signal envelope obtained according to the EWOA_VMD method is 6.868 9 lower, the convergence speed of each parameter is significantly improved, the signal-to-noise ratio of processed signals is higher and the denoising effect is better. Finally, the effectiveness of the proposed MSDPE feature is verified. Through calculating the MSDPE value within the range of scale factor 1-6, it is found that when the scale factor is 4, the MSDPE values of different wear states have obvious differences, and the change curve can accurately reflect the time of abrasive wear. The correlation between the commonly used time domain, frequency domain and MSDPE characteristics and the wear volume of the abrasive particle is further analyzed. When the scale factor is 4, the correlation coefficient between the MSDPE and the wear volume of the abrasive particle reaches 0.92, which is significantly higher than other characteristic parameters. The results show that the MSDPE feature is highly sensitive to the wear behavior of the abrasive particle, and can accurately identify the occurrence of wear events. It can be used as an effective characterization parameter for the acoustic emission monitoring of grinding wheel wear, which provides a new solution for the grinding wheel wear state monitoring process.
关键词
单颗磨粒 /
磨损机制 /
声发射 /
增强鲸鱼优化算法 /
特征提取 /
多尺度标准差排列熵
Key words
single abrasive particle /
wear mechanism /
acoustic emission /
enhanced whale optimization algorithm (EWOA) /
feature extraction /
multi-scale standard deviation permutation entropy (MSDPE)
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基金
湖南省自然科学基金项目（2024JJ8244）; 企业技术开发项目（D12591）
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