Development of a tool wear-monitoring system for hard turning |
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| Authors: | C. Scheffer H. Kratz P. S. Heyns F. Klocke |
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| Affiliation: | a Dynamic Systems Group (DSG), Department of Mechanical and Aeronautical Engineering, University of Pretoria, Pretoria, ZA-0002, South Africa;b Laboratory for Machine Tools and Production Engineering (WZL), Chair of Manufacturing Technology, RWTH Aachen University of Technology, Aachen, Germany |
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| Abstract: | This paper describes an in-depth study on the development of a system for monitoring tool wear in hard turning. Hard turning is used in the manufacturing industry as an economic alternative to grinding, but the reliability of hard turning processes is often unpredictable. One of the main factors affecting the reliability of hard turning is tool wear. Conventional wear-monitoring systems for turning operations cannot be used for monitoring tools used in hard turning because a conglomeration of phenomena, such as chip formation, tool wear and surface finish during hard turning, exhibits unique behavior not found in regular turning operations. In this study, various aspects associated with hard turning were investigated with the aim of designing an accurate tool wear-monitoring system for hard turning. The findings of the investigation showed that the best method to monitor tool wear during hard turning would be by means of force-based monitoring with an Artificial Intelligence (AI) model. The novel formulation of the proposed AI model enables it to provide an accurate solution for monitoring crater and flank wear during hard turning. The suggested wear-monitoring system is simple and flexible enough for online implementation, which will allow more reliable hard turning in industry. |
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| Keywords: | Process monitoring Tool wear Hard turning Cutting forces Neural networks |
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