Abstract
This research focuses on to develop monitoring systems that can detect surface roughness by using adaptive neuro-fuzzy inference system (ANFIS) for the unmanned production system. Cutting force is one important characteristic variable to be monitored in the cutting processes to determine tool life regarding tool breakage, tool wear, and surface roughness (Ra) of the workpiece. The principal presumption was that the cutting forces are normally increased by the wear of the tool. Therefore, the ANFIS method is used to extract the features of tool states from cutting force signals. Input parameters for making an ANFIS model are Speed, feed, depth of cut, cutting force and output in term of surface roughness. A piezoelectric dynamometer measured the forces. The experimental forces and surface roughness were utilized to train the developed simulation environment based on ANFIS modeling. By tool condition monitoring system, the machining process can be on-line monitored for the unmanned production system. The achieved Correlation coefficient (R) is 0.9528 and average percentage error is 7.38 %. In this research, we predict the surface roughness of a workpiece by using the ANFIS modeling and surface roughness can be used for tool life management and enables it for monitoring of unmanned production system.
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Acknowledgments
We would like to thank everyone that participated in this research work, in particular Mr. Pankaj Jain, Mr. Kailash Yadav, Mr. Amarjeet Singh, and Mr. Achin Jain from JCB India Limited, New Holland Fiat India Pvt. Limited, Maruti Suzuki India Limited, and Tata Motors Limited respectively. A special thanks to Mr. Sanoop Kottuvayal Thazha Kuni, Garrison engineer for the help in methodology. We thank all the anonymous reviewers of this paper for his or her valuable suggestions, who have helped to improve the quality of this paper.
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Jain, V., Raj, T. Tool life management of unmanned production system based on surface roughness by ANFIS. Int J Syst Assur Eng Manag 8, 458–467 (2017). https://doi.org/10.1007/s13198-016-0450-2
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DOI: https://doi.org/10.1007/s13198-016-0450-2