Issue 70

P. Kulkarni et alii, Frattura ed Integrità Strutturale, 70 (2024) 71-90; DOI: 10.3221/IGF-ESIS.70.04

triangular MF. Additionally, Tab. 5 displays the testing error for the training and testing data for each response, along with the corresponding R-squared values, when using the triangular, trapezoidal, Gaussian, and generalized bell membership functions. The lowest testing error with the better R-squared value close to one can be seen for the triangular membership function, followed by the Gaussian (Gaussmf) membership function for the ANFIS cutting force and surface roughness models. However, no significant difference can be seen in the testing error or R-squared value for the different MFs considered in the present study. The highest R-squared value and the lowest testing error can be seen with the triangular membership function. In general, this study found better prediction accuracy for the responses with the triangular membership function. However, this study finds scope for further improvement in the prediction accuracy of the ANFIS surface roughness model, considering hybrid optimization methods for fine-tuning the FIS parameters, such as membership functions and rule weights.

Figure 13: Developed ANFIS models for Fc , Ra , and TL .

Figure 14: Mapping of training data and testing data with FIS output for Fc , Ra , and TL .

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