Application of audible sound signals for tool wear monitoring using machine learning techniques in end milling

The machining process monitoring, especially the tool wear monitoring, is very critical in modern automated gear machining environment which needs instant detection of cutting tool state and/or process conditions, quick final diagnosis and appropriate actions. It has been realized that the non-uniform hardness of the workpiece material due to the improper heat treatment can cause expedited tool wear and unexpected tool breakage, which greatly increases difficulties and complexities in monitoring the tool conditions in gear cutting. This paper provides a solution to detect the wear conditions of the gear milling cutter in the cutting of workpiece materials with hardness variations using the audible sound signals. In this study, cutting tools and workpieces are prepared to have different flank wear classes and hardness variations respectively. A series of gear milling experiments are operated with a broad range of cutting conditions to collect sound signals. A machine learning algorithm that incorporates support vector machine (SVM) approach coupled with the application of time and frequency domain analysis is developed to correlate observed sound signals’ signatures to specified tool wear classes and workpiece hardness levels. The performance evaluation results of the proposed monitoring system have shown accurate predictions in detecting tool wear conditions and workpiece hardness variations from the sound signals in gear milling.

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Tool Wear Monitoring of Multipoint Cutting Tool using Sound Signal Features Signals with Machine Learning Techniques

Materials Today Proceedings ◽

10.1016/j.matpr.2018.11.014 ◽

2018 ◽

Vol 5 (11) ◽

pp. 25720-25729 ◽

Cited By ~ 3

Author(s):

S. Ravikumar ◽

K.I. Ramachandran

Keyword(s):

Machine Learning ◽

Tool Wear ◽

Cutting Tool ◽

Sound Signal ◽

Machine Learning Techniques ◽

Tool Wear Monitoring ◽

Signal Features ◽

Learning Techniques ◽

Wear Monitoring

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Tool wear monitoring by machine learning techniques and singular spectrum analysis

Mechanical Systems and Signal Processing ◽

10.1016/j.ymssp.2010.07.014 ◽

2011 ◽

Vol 25 (1) ◽

pp. 400-415 ◽

Cited By ~ 55

Author(s):

Bovic Kilundu ◽

Pierre Dehombreux ◽

Xavier Chiementin

Keyword(s):

Machine Learning ◽

Tool Wear ◽

Spectrum Analysis ◽

Singular Spectrum Analysis ◽

Machine Learning Techniques ◽

Tool Wear Monitoring ◽

Singular Spectrum ◽

Learning Techniques ◽

Wear Monitoring

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Cutting Process Monitoring System Using Audible Sound Signals and Machine Learning Techniques: An Application to End Milling

Volume 3: Manufacturing Equipment and Systems ◽

10.1115/msec2017-3069 ◽

2017 ◽

Cited By ~ 1

Author(s):

Achyuth Kothuru ◽

Sai Prasad Nooka ◽

Rui Liu

Keyword(s):

Machine Learning ◽

Tool Wear ◽

Monitoring System ◽

Learning Algorithm ◽

End Milling ◽

Cutting Tools ◽

Frequency Domain Analysis ◽

Machine Learning Techniques ◽

Support Vector ◽

Audible Sound

In a fully automated manufacturing system, tool condition monitoring system is essential to detect the failure in advance and minimize the manufacturing loses with the increase in productivity. To look for a reliable, simple and cheap solution, this paper proposes a new tool wear monitoring model to detect the tool wear progression and early detection of tool failure in end milling using audible sound signals. In this study, cutting tools are classified into six classes based on different flank wear ranges. A series of end milling experiments are operated with a broad range of cutting conditions for each class to collect sound signals. A machine learning algorithm that incorporates support vector machine (SVM) approach coupled with the application of time and frequency domain analysis is developed to correlate observed sound signals’ signatures to tool wear conditions. The performance evaluation results of the proposed algorithm have shown accurate predictions in detecting tool wear conditions from the sound signals. In addition, the proposed machine learning approach has shown the fastest response rate, which provides the good solution for on-line cutting tool monitoring.

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