To address the above-mentioned issues, several improved BPNN algorithms have been proposed. Cao et al. proposed a Genetic Algorithm optimized BPNN (GA-BPNN), where GA is used to find the optimal initial weight and threshold for the BPNN and reduce the risk of convergence to a local optimum. This method predicted the milled surface roughness S q and fractal dimension D s with a prediction accuracy >90.39%. The regression analysis results in Fig. (c) showed that correlation coefficient R was higher than 0.94, indicating a strong fitting accuracy the proposed method. Wang et al. developed an adaptive ANN (aANN), which can adjust the learning rate and momentum gradient to enhance the prediction stability. The novel aANN exhibited better accuracy than traditional ANN in predicting grinding time and energy consumption, with R 2 =0.9466 vs. R 2 =0.8017. To further overcome the limitations of BPNN, alternative network architectures such as RBFNN and ANFIS have also been developed. Unlike BPNN which uses global activation functions (e.g. sigmoid) and relies on gradient-based optimization, RBFNN employs radial basis functions as activation functions. This local approach enables RBFNN to model non-linear relationships more effectively, particularly in scenarios where the inputoutput mapping involves complex or highly localized patterns. Tsao used RBFNN to predict the CFRP drilling thrust force under drill bit step angle, stage ratio, feed rate and spindle speed. Experimental validation confirmed that RBFNN has a much lower prediction error (0.3%) compared to that of PR (28%). Jia et al. deployed RBFNN to predict the cutting force and thrust force in orthogonal cutting of CFRP under various tool rake angle, clearance angle, depth of cut and fibre orientation. The effectiveness of RBFNN has also been reported in previous studies . In addition to RBFNN, ANFIS utilizes a hybrid learning approach that combines gradient descent for optimizing neural network parameters with least-squares estimation for finetuning fuzzy rules. This dual optimization strategy significantly enhances its learning capability compared to BPNN . Ozden et al. deployed ANFIS to predict the cutting force in turning of reinforced PEEK with 30 vol% short carbon/glass fibre, considering variables such as feed rate, spindle speed, cutting tool and type of reinforcement material. ANFIS showed better prediction performance than BPNN (error 4.68% vs. 8.71%).