Prediction and control of the surface roughness for the end milling process using ANFIS

  • Ali Abdulshahed Electrical & Electronic Engineering Department, Misurata University, Libya
  • Ibrahim Badi Mechanical Engineering Department, Misurata University, Libya
Keywords: ANFIS, Surface Roughness, Computer Numerical Control (CNC) Machine

Abstract

In this paper, we applied an Adaptive Neuro-Fuzzy Inference System (ANFIS) approach for prediction of the workpiece surface roughness for the end milling process. A small number of fuzzy rules were used for building ANFIS models with the help of Subtractive clustering method (ANFIS-Subtractive clustering model). The predicted values are found to be in excellent agreement with the experimental data with average error values in the range of 3.47-3.49%. Also, we compared the proposed ANFIS models to other Artificial intelligence (AI) approaches. Results show that the proposed model has high accuracy in comparison to other AI approaches in literature. Therefore, we can use ANFIS model to predict the workpiece surface roughness for the end milling process.

References

1. Chandrasekaran, M., et al., Application of soft computing techniques in machining performance prediction and optimization: a literature review. The International Journal of Advanced Manufacturing Technology, 2010. 46(5-8): p. 445-464.
2. Markopoulos, A., D. Manolakos, and N. Vaxevanidis, Artificial neural network models for the prediction of surface roughness in electrical discharge machining. Journal of Intelligent Manufacturing, 2008. 19(3): p. 283-292.
3. Kovac, P., et al., Application of fuzzy logic and regression analysis for modeling surface roughness in face milliing. Journal of Intelligent Manufacturing, 2013. 24(4): p. 755-762.
4. Maher, I., et al., Cutting force-based adaptive neuro-fuzzy approach for accurate surface roughness prediction in end milling operation for intelligent machining. The International Journal of Advanced Manufacturing Technology, 2014: p. 1-9.
5. Lo, S.-P., An adaptive-network based fuzzy inference system for prediction of workpiece surface roughness in end milling. Journal of materials processing technology, 2003. 142(3): p. 665-675.
6. Ho, W.-H., et al., Adaptive network-based fuzzy inference system for prediction of surface roughness in end milling process using hybrid Taguchi-genetic learning algorithm. Expert Systems with Applications, 2009. 36(2, Part 2): p. 3216-3222.
7. Dong, M. and N. Wang, Adaptive network-based fuzzy inference system with leave-one-out cross-validation approach for prediction of surface roughness. Applied Mathematical Modelling, 2011. 35(3): p. 1024-1035.
8. Sharkawy, A.B., Prediction of surface roughness in end milling process using intelligent systems: a comparative study. Applied Computational Intelligence and Soft Computing, 2011. 2011: p. 8.
9. Guillaume, S., Designing fuzzy inference systems from data: An interpretability-oriented review. Fuzzy Systems, IEEE Transactions on, 2001. 9(3): p. 426-443.
10. Jang, J.S.R., ANFIS: Adaptive-network-based fuzzy inference system. Systems, Man and Cybernetics, IEEE Transactions on, 1993. 23(3): p. 665-685.
11. Haddad, H. and M. Al Kobaisi, Optimization of the polymer concrete used for manufacturing bases for precision tool machines. Composites Part B: Engineering, 2012. 43(8): p. 3061-3068.
12. Chiu, S.L., Fuzzy model identification based on cluster estimation. Journal of intelligent and Fuzzy systems, 1994. 2(3): p. 267-278.
Published
2018-12-19
How to Cite
Abdulshahed, A., & Badi, I. (2018). Prediction and control of the surface roughness for the end milling process using ANFIS. Operational Research in Engineering Sciences: Theory and Applications, 1(1), 1-12. Retrieved from http://www.oresta.org/index.php/oresta/article/view/1