Prediction of cutting force by using ANFIS

Original Article

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%.

Keywords

Cutting force Modelling Cutting force estimation ANFIS 

Notes

Acknowledgements

We would like to thank everyone that participated in this research work. We express our gratitude all the anonymous reviewers of this paper for their valuable suggestions, who have helped to improve the quality of this paper.

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Copyright information

© The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2018

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringAmity University HaryanaGurgaonIndia
  2. 2.Department of Mechanical EngineeringYMCA University of Science and TechnologyFaridabadIndia

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