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A Study on Cyber-physical System Architecture to Predict Cutting Tool Condition in Machining

Yasuo Kondo 1, Mitsugu Yamaguchi 2, Satoshi Sakamoto 3, and Kenji Yamaguchi 3
1. Yamagata University/Department of Mechanical System Engineering, 4-3-16 Jhonan Yonezawa, Japan
2. University of Tokyo/Institute of Industrial Science, Kashiwanoha Kashiwa Chiba, Japan
3. Yokohama National University/Department of Education and Human Science, Tokiwadai Yokohama, Japan

Abstract— This article illustrates a systematic approach for predicting tool wear in machining process through Cyber-Physical System (CPS) architecture using simple electronic components such as personal computers and low-cost sensors. The proposed Cyber-Physical structure consists of 5 steps; smart connection, data to information, feature extraction, awareness of issues and self-adjustment. We tried to install a big data analysis technology into CPS architecture to catch the usual/unusual state of the cutting tool from the spindle power consumption changes. The excessive repetitions of grooving would bring the trend changing of power consumption. To facilitate the statistical analysis, the correlation coefficient R was calculated from the single regression analysis between two different cycles of time-series power consumption. The correlation coefficient R also had a strong relation with the condition changes of tool wear and would become a powerful tool to catch the usual/unusual state of the cutting tool in the proposed CPS architecture. The health information obtained from the system can be used for higher level of management of cutting tool based on the condition monitoring free from the schedule-based maintenance. 

Index Terms—cyber-physical system, industry 4.0, Society 5.0, big data analytics, tool wear, predictive maintenance

Cite: Yasuo Kondo, Mitsugu Yamaguchi, Satoshi Sakamoto, and Kenji Yamaguchi, "A Study on Cyber-physical System Architecture to Predict Cutting Tool Condition in Machining" International Journal of Mechanical Engineering and Robotics Research, Vol. 9, No. 4, pp. 565-569, April 2020. DOI: 10.18178/ijmerr.9.4.565-569

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.