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Prediction of cutting force by using ANFIS

Author

Listed:
  • Vineet Jain

    (Amity University Haryana)

  • Tilak Raj

    (YMCA University of Science and Technology)

Abstract

The aim of this research is to develop a model to predict the cutting forces of a turning operation. This paper focuses on to design a monitoring system that can recognize cutting force on the basis of cutting parameters like spindle speed, feed and depth of cut by using adaptive neuro-fuzzy inference system (ANFIS). Cutting force is one of the important characteristic variables to be watched and controlled in the cutting processes to determine tool life and surface roughness of the work piece. The principal assumption was that the cutting forces increase due to the wearing of the tool. So, ANFIS model is used to express the cutting force signal. In this paper, ANFIS is used to predict the cutting force. The correlation coefficient (R) and average percentage error found in this modeling are 0.9976 and 2.59% respectively. The predicted cutting force values derived from ANFIS were compared with experimental data. The comparison indicates that the ANFIS achieved very satisfactory accuracy. The correlation coefficient (R) and average percentage error found in this modeling are 0.9976 and 2.59% respectively. The prediction accuracy of ANFIS reached is as high as 97%.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:ijsaem:v:9:y:2018:i:5:d:10.1007_s13198-018-0717-x
    DOI: 10.1007/s13198-018-0717-x
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    References listed on IDEAS

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    1. 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.
    2. Himanshu Chaudhary & Vikas Panwar & Rajendra Prasad & N. Sukavanam, 2016. "Adaptive neuro fuzzy based hybrid force/position control for an industrial robot manipulator," Journal of Intelligent Manufacturing, Springer, vol. 27(6), pages 1299-1308, December.
    3. 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.
    Full references (including those not matched with items on IDEAS)

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