Application of adaptive neuro-fuzzy inference system for predicting surface roughness in turning AISI 304 steel

https://doi.org/10.31130/ud-jst.2025.573

Abstract: 266 | PDF: 198

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Author

  • Tran Cong Chi
    Vietnam National University of Forestry, Vietnam

Keywords:

Surface roughness
ANFIS
Predicting model
Turning
AISI 304 Steel

Abstract

This study examines the influence of three turning parameters: cutting depth (t), feed rate (f), and cutting speed (n) on Ra, while also developing a predictive model for machining AISI 304 steel using Adaptive Neuro-Fuzzy Inference System (ANFIS). The results of the ANOVA analysis indicate that, all three cutting parameters have a significant impact on Ra, with the feed rate (f) having the most effective influence, emphasizing the role of f in controlling surface roughness. The ANFIS predictive model was developed using two training methods, Hybrid and Backpropagation, corresponding to eight different membership functions. The results show that the Hybrid training model using the Gaussmf membership function achieved the highest coefficient of determination (R²) of 0.986081 and the lowest root mean square error (RMSE) of 0.013055. These results demonstrate that, the ANFIS model can predict Ra with relatively high accuracy based on the machining parameters.

References

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Published

Feb 28, 2025
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How to Cite
Chi, T. C. “Application of Adaptive Neuro-Fuzzy Inference System for Predicting Surface Roughness in Turning AISI 304 Steel”. The University of Danang - Journal of Science and Technology, vol. 23, no. 2, Feb. 2025, pp. 88-92, doi:10.31130/ud-jst.2025.573.

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