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Tool life management of unmanned production system based on surface roughness by ANFIS

Author

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  • Vineet Jain

    (Amity University Gurgaon)

  • Tilak Raj

    (YMCA University of Science and Technology)

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.

Suggested Citation

  • Vineet Jain & Tilak Raj, 2017. "Tool life management of unmanned production system based on surface roughness by ANFIS," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 458-467, June.
    • Handle: RePEc:spr:ijsaem:v:8:y:2017:i:2:d:10.1007_s13198-016-0450-2
    DOI: 10.1007/s13198-016-0450-2
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    References listed on IDEAS

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    1. Jain, Vineet & Raj, Tilak, 2016. "Modeling and analysis of FMS performance variables by ISM, SEM and GTMA approach," International Journal of Production Economics, Elsevier, vol. 171(P1), pages 84-96.
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    Cited by:

    1. Dragan Rodić & Milenko Sekulić & Marin Gostimirović & Vladimir Pucovsky & Davorin Kramar, 2021. "Fuzzy logic and sub-clustering approaches to predict main cutting force in high-pressure jet assisted turning," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 21-36, January.
    2. Vineet Jain & Tilak Raj, 2018. "Prediction of cutting force by using ANFIS," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(5), pages 1137-1146, October.

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