Tool condition monitoring using K-star algorithm |
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| Affiliation: | 1. School of Mechanical and Building Science, VIT University, Chennai 600127, India;2. Department of Mechanical Engineering, Amrita School of Engineering Amrita Vishwa Vidyapeetham, Coiambatore, India;1. School of Management, Fuzhou University, Fuzhou, Fujian 350108, China;2. College of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330013, China;1. Research Center of Information and Control, Dalian University of Technology, Dalian 116024, China;2. School of Mathematics and System Science, Shenyang Normal University, Shenyang 110034, China;3. Department of Electrical and Computer Engineering, University of Alberta, Edmonton T6R 2V4, AB, Canada;1. Department of Computer Science and Engineering, University of Dhaka, Bangladesh;2. Department of Computer Engineering, Kyung Hee University, South Korea |
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| Abstract: | Cutting tools are required for day to day activities in manufacturing. Continuous machining operations lead tool to undergo wear. Worn out tools effect surface finish during machining. The dimensional accuracy of components is also compromised. Robust tool health is vital for better productivity. Hence, an online system condition monitoring of tools is the need of hour, promising reduction in maintenance cost with a greater productivity saving both time and money. This paper presents the classification performance of K-star algorithm. A set of statistical features extracted from vibration signals (good and faulty conditions) form the input to algorithm. In the present study, the K-star algorithm is able to achieve 78% classification accuracy. |
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| Keywords: | Machine learning K-star Tool condition monitoring Tool wear Vibration signals |
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