Publication Type:

Journal Article

Source:

International Journal of Computational Intelligence and Applications, World Scientific Publishing Co., Volume 17, Number 3 (2018)

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85052911004&doi=10.1142%2fS1469026818500177&partnerID=40&md5=11ec304a56c60990acf2643712d5d868

Keywords:

Acoustic emission signal, Acoustic emission testing, Acoustic emissions, Artificial intelligence, Biomedical industry, Carbide cutting tools, Classification ability, Cutting tools, Data mining, Decision trees, Frequency domain analysis, High Speed machining, Learning algorithms, Learning systems, Machine learning approaches, Machining, Statistical features, Support vector machines, Surface roughness, Time and frequency domains, Titanium alloys, Tool wear, Trees (mathematics), Tungsten carbide, Wear of materials

Abstract:

Mechanical and chemical properties of titanium alloy have led to its wide range of applications in aerospace and biomedical industries. The heat generation and its transfer from the cutting zone are critical in machining of titanium alloys. The process of transferring heat from the primary cutting zone is difficult due to poor thermal conductivity of titanium alloy, and it will lead to rapid tool wear and poor surface finish. An effective tool monitoring system is essential to predict such variations during machining process. In this study, using a high-speed precision mill, experiments are conducted under optimum cutting conditions with an objective of maximizing the life of tungsten carbide tool. Tool wear profile is established and tool conditions are arrived on the basis of the surface roughness. Acoustic emission (AE) signals are captured using an AE sensor during machining of titanium alloy. Statistical features are extracted in time and frequency domain. Features that contain rich information about the tool conditions are selected using J48 decision tree (DT) algorithm. Tool condition classification abilities of DT and support vector machines are studied in time and frequency domains. © 2018 World Scientific Publishing Europe Ltd.

Notes:

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Cite this Research Publication

Krishna Kumar P., Rameshkumar, K., and Ramachandran, K. I., “Acoustic Emission-Based Tool Condition Classification in a Precision High-Speed Machining of Titanium Alloy: A Machine Learning Approach”, International Journal of Computational Intelligence and Applications, vol. 17, 2018.