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Experimental Investigation and ANFIS-Based Modelling During Machining of EN31 Alloy Steel
Ishwer Shivakoti1, Lewlyn L R Rodrigues2, Robert Cep3
1Department of Mechanical Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majhitar 737136, India.
Materials (Basel, Switzerland)
|July 18, 2020
Summary
Machining control variables significantly impact EN31 alloy steel turning performance. An adaptive neuro fuzzy inference system (ANFIS) accurately predicts material removal rate, surface roughness, and cutting force.
Area of Science:
- Materials Science and Engineering
- Manufacturing Engineering
- Computational Intelligence
Background:
- Machining EN31 alloy steel with advanced coated tools requires understanding parameter effects.
- Optimizing turning processes involves analyzing material removal rate (MRR), surface roughness (Ra), and cutting force (Fc).
Purpose of the Study:
- To investigate the parametric effect of feed, revolutions per minute (RPM), and depth of cut on turning EN31 alloy steel.
- To develop and validate an adaptive neuro fuzzy inference system (ANFIS) for predicting process responses.
Main Methods:
- Experimental turning of EN31 alloy steel using Chemical Vapor Deposited (CVD) Ti(C,N) + Al2O3 + TiN coated carbide inserts.
- Systematic variation of feed, RPM, and depth of cut across four levels.
- Development and application of an ANFIS model for response prediction.
Main Results:
- Machining parameters significantly influence MRR, Ra, and Fc.
- The ANFIS model demonstrated high accuracy in predicting process responses.
- Correlation coefficients for MRR, Ra, and Fc were 0.99, 0.98, and 0.964, respectively.
Conclusions:
- Turning control variables critically affect the efficiency and quality of EN31 alloy steel machining.
- The ANFIS model provides a reliable tool for predicting and optimizing machining parameters.
- This research offers valuable insights for enhancing CNC machining operations.
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