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

• Published in: International journal of advanced manufacturing technology Vol. 126; no. 7-8; pp. 3069 - 3081
• Main Authors: Cardoz, Basil, Shaikh, Haris Naiyer E Azam, Mulani, Shoaib Munir, Kumar, Ashwani, Rajasekharan, Sabareesh Geetha
• Format: Journal Article
• Language: English
• Published: London Springer London 01-06-2023; Springer Nature B.V
• Subjects: Acoustics; Advanced manufacturing technologies; Algorithms; Artificial intelligence; CAE) and Design; Classification; Computer-Aided Engineering (CAD; Condition monitoring; Costs; Cutting tools; Engineering; Image processing; Industrial and Production Engineering; Internet of Things; Machine learning; Manufacturing; Measurement techniques; Mechanical Engineering; Media Management; Neural networks; Original Article; Production costs; Productivity; Sensors; Surface finish; Tool wear; Turning (machining); Vibration; Workflow; Condition monitoring; Image processing; Random forests; Tool wear; Machine learning
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-023-11173-y

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.