Tool Wear Condition Monitoring Using Emitted Sound Signals By Simple Machine Learning Technique

C. L. ,. Perumal, S. B. ,. Bhadrinathan, A. Samraj
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Abstract

As a continuous enhancement to the tool wear monitoring using non-disturbing method of sound wave analysis, a simple machine learning technique enhances the prediction to better levels and reduces the procedures. A simple linear regression Algorithm was used to train and predict the trends of various degrees of tool wear to distinguish them from each other. The results based on this simple linear regression were successful in showing the difference of sound patterns and are reported.
基于简单机器学习技术的刀具磨损状态监测
作为使用无干扰声波分析方法的工具磨损监测的不断增强,一种简单的机器学习技术将预测提高到更好的水平并减少了程序。采用简单的线性回归算法对刀具不同磨损程度的变化趋势进行训练和预测,以区分刀具不同磨损程度的变化趋势。基于这种简单线性回归的结果成功地显示了声音模式的差异,并被报道。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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