Some earlier studies have shown that various modeling and optimization methods, combined with neural networks, can improve different output performances in hard turning ([PERSON] et al., 2018). [PERSON] et al. (2018) used RSM and artificial neural networks (ANNs) for output modeling. Process parameter optimization was carried out using the desirability function, aiming to minimize both surface roughness and cutting forces simultaneously. The results revealed that feed rate had a significant impact on surface roughness, while depth of cut was found to be the most influential factor for cutting forces. Both the ANN and RSM models showed strong correlation with experimental data, with the ANN models demonstrating better accuracy. [PERSON] et al. (2018) conducted a study to model the cutting forces generated during the turning of AISI 316L austenitic stainless steel, using ANN and multiple regression analysis. The model's input parameters included cutting speed, feed rate, and coating type. The results indicated that ANN provided better accuracy in predicting cutting forces compared to the multiple regression method, making it the preferred approach according to the authors. In study ([PERSON] et al., 2018), a new hybrid approach, the PSO-RDNN algorithm, was introduced, combining particle swarm optimization (PSO) with a recurrent dynamic neural network (RDNN) to optimize multiple performance parameters in the finishing turning of hardened AISI D2 steel. Process parameters, such as cutting speed and feed rate, are optimized to reduce operational costs, extend tool life, and achieve acceptable surface roughness. Based on experimental data, two neural network models were developed to predict tool flank wear and surface roughness during machining. Using these trained neural networks along with a structured hybrid algorithm, the optimal cutting parameters were determined. In another study ([PERSON] et al., 2018), a hybrid Taguchi-ANN approach was used in order to predict and optimize tool wear for turning EN24 material. This study demonstrated that an ANN combined with the Taguchi philosophy can be successfully used for optimizing and predicting tool wear. Many recent studies also recommend the use of artificial neural networks in order to improve performance response in practical applications. [PERSON] et al. (2018) suggest using a combination of ANN and GA methodology to find the best machining process parameters and achieve a good performance response in practical applications. Optimizing surface roughness using genetic algorithms and artificial neural networks was also performed in paper ([PERSON] et al., 2018). Minimum surface roughness is obtained after application of genetic algorithms. [PERSON] et al. (2018) developed regression models and an artificial neural network model to fuse the cutting force, cutting temperature, tool wear, and displacement of tool vibration to predict surface roughness. From the results, it was observed that the prediction of surface roughness by the ANN had a higher accuracy than the regression model. Additionally, the predictive accuracy of artificial neural networks can be improved by using different training algorithms. Study ([PERSON] et al., 2018) evaluated the application of the Levenberg-Marquardt (LM) and scaled conjugate gradient (SCG) training algorithms of artificial neural networks in the modeling and prediction of metal removal rate (MRR) and average surface roughness (Ra) in the turning of cylindrical mild steel. [PERSON] et al. (2018) examined the effects of machining parameters on machined surfaces to determine the optimal turning parameters for AISI-316 under a dry machining environment. Among three machine-learning (ML) models, viz. support vector regression (SVR), Gaussian process regression (GPR), and gradient boosting regression (GBR), GBR yielded the best results, with significantly higher R\({}^{2}\) scores and lower RMSE values.