Random forests based classification of tool wear using vibration signals and wear area estimation from tool image data

被引：0
作者：
Basil Cardoz
Haris Naiyer E Azam Shaikh
Shoaib Munir Mulani
Ashwani Kumar
Sabareesh Geetha Rajasekharan
机构：
[1] Birla Institute of Technology and Science - Pilani,Department of Mechanical Engineering
来源：
The International Journal of Advanced Manufacturing Technology | 2023年 / 126卷
关键词：
Tool wear; Condition monitoring; Random forests; Image processing; Machine learning;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In precision manufacturing, tool condition monitoring is critical for improving surface finish, increasing efficiency, and lowering manufacturing costs. The present work discusses a complete workflow to accurately predict the tool condition based on vibration data obtained during the turning operation performed on a lathe. An image processing methodology is applied to compute the tool wear area. A specialized misclassification cost matrix is used to train the random forests algorithm to improve the classification of tool conditions. This model can correctly classify tool condition from vibration signals of 0.5 s with an accuracy of 97%. Furthermore, this investigation can be modified to suit the real-world classification of the tool condition based on tool wear requirements.
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收藏
页码：3069 / 3081
页数：12
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