Operational Research in Engineering Sciences

Journal DOI: https://doi.org/10.31181/oresta190101s

(A Journal of Management and Engineering) ISSN 2620-1607 | ISSN 2620-1747 |

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

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.

Keywords
ANFIS, Surface Roughness, Computer Numerical Control (CNC) Machine.

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SCImago Journal & Country Rank

CiteScore for Management Science and Operations Research

8.1
2021CiteScore
 
 
89th percentile
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CiteScore for Engineering (miscellaneous)

8.1
2021CiteScore
 
 
93rd percentile
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