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在精密磨削加工过程中,为了使砂轮保持锐利程度及正确的几何形状,需要对砂轮及时修整。如何有效预测金刚笔修整磨损一直是磨削加工中的一个技术难题。根据金刚笔修整磨损机理分析,提出了基于声发射信号的串行优化算法支持向量机(SMO-SVM)金刚笔修整磨损预测方法,利用小波包算法对声发射信号特征信息进行提取,建立了将小波包提取的声发射信号特征量作为的输入SMO-SVM的金刚笔磨损预测模型;实验结果表明基于声发射信号的SMO-SVM模型对金刚笔磨损前后的预测准确性达到95.257 1%以上。
In the precision grinding process, in order to keep the wheel sharp and the correct geometry, the wheel needs to be trimmed in time. How to effectively predict diamond pencil repair wear has always been a technical problem in grinding. According to the mechanism analysis of diamond pen trimming and wear, a serial optimization algorithm support vector machine (SMO-SVM) diamond pencil trimming wear prediction method based on acoustic emission signal is proposed. By using wavelet packet algorithm to extract the characteristic information of acoustic emission signal, The results show that SMO-SVM model based on acoustic emission signal has a predictive accuracy of more than 95.257 1% before and after diamond pen wear.