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

Authors

  • Ali Abdulshahed Electrical & Electronic Engineering Department, Misurata University, Libya
  • Ibrahim Badi Mechanical Engineering Department, Misurata University, Libya

DOI:

https://doi.org/10.31181/oresta1901201011a

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.

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References

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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. https://doi.org/10.31181/oresta1901201011a