This paper deals with the prediction of surface roughness in manufacturing polycarbonate (PC) by applying Bayesian optimization for machine learning models. The input variables of ultraprecision turningnamely, feed rate, depth of cut, spindle speed, and vibration of the X-, Y-, and Z-axisare the main factors affecting surface quality. In this research, six machine learning- (ML-) based modelsartificial neural network (ANN), Cat Boost Regression (CAT), Support Vector Machine (SVR), Gradient Boosting Regression (GBR), Decision Tree Regression (DTR), and Extreme Gradient Boosting Regression (XGB)were applied to predict the surface roughness (Ra). The predictive performance of the baseline models was quantitatively assessed through error metrics: root means square error (RMSE), mean absolute error (MAE), and coefficient of determination (R²). The overall results indicate that the XGB and CAT models predict Ra with the greatest accuracy. In improving baseline models such as XGB and CAT, the Bayesian optimization (BO) is next used to determine their best hyperparameters, and the results indicate that XGB is the best model according to the evaluation metrics. Results have shown that the performance of the models has been improved significantly with BO. For example, the values of RMSE and MAE of XGB have decreased from 0.0076 to 0.0047 and from 0.0063 to 0.0027, respectively, for the training dataset. Using the testing dataset, the values of RMSE and MAE of XGB have decreased from 0.4033 to 0.2512 and from 0.2845 to 0.2225, respectively. Moreover, the vibrations of the X, Y, and Z axes and feed rate are the most significant feature in predicting the results, which is in high accordance with the literature. We find that, in a specified value domain, the vibration of the axes has a greater influence on the surface quality than does the cutting condition.

This work proposes a rapid and robust machine-learning model to predict the volumetric error of a five-axis machine tool. For this purpose, several machine learning models – which are MultiOutput regression, Chained MultiOutput regression, Linear regression, SVM regression, XGB regression and ANN regression – have been selected, and their performances were compared to other models in the literature and to one another to find the best model for the problem at hand. The robustness of each model is investigated using three statistical metrics: namely Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Coefficient of Determination (R²). The results show that the models proposed in this paper are more effective than those in the literature, using the same data. Amongst the proposed models, the SVR Regression model has proven to be the best, considering all statistical metrics. Compared to the polynomial method in the literature with varying order levels, the method proposed here improves accuracy and predictive performance by 27%, with an RMSE of 0.03246 mm for the SVR model. Finally, based on the best model, an explicit equation has been deduced for practical applications. That prediction equation has been implemented in Excel and included in this paper.

This work focuses on optimizing process parameters in turning AISI 4340 alloy steel. A hybridization of Machine Learning (ML) algorithms and a Non-Dominated Sorting Genetic Algorithm (NSGA-II) is applied to find the Pareto solution. The objective functions are a simultaneous minimum of average...

This work addresses the prediction and optimization of average surface roughness (Ra) and maximum flank wear (Vbmax) of 6061 aluminum alloy during high-speed milling. The investigation was done using a DMU 50 CNC 5-axis machine with Ultracut FX 6090 fluid. Four factors were examined: the table feed rate, cutting speed, depth of cut, and cutting length. Three levels of each factor were examined to conduct 81 experiment runs. The response parameters in these experiments were measurements of Ra and Vbmax. We applied a two-pronged approach that combines machine learning (ML) and a Nondominated Sorting Genetic Algorithm (NSGA-II) to model and optimize Ra and Vbmax. Four ML models were used to predict Ra and Vbmax: linear regression (LIN), support vector machine regression (SVR), a gradient boosting tree (GBR), and an artificial neural network (ANN). The input variables were the significant factors that affect the surface quality and tool wear: the feed rate, depth of cut, cutting speed, and cutting time. Several quality metrics were employed to quantify the performance of the models, such as the root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). As a result, SVR and ANN were found to have the best predictive performance for Ra and Vbmax. These models and the NSGA-II-based approach were then employed for multiobjective optimization of cutting parameters during high-speed milling of aluminum 6061. Fifty Pareto solutions were found with Ra in the range of 0.257 to 0.308µm and Vbmax in the range of 136.198 to 137.133μm. Experimental validations were then conducted to confirm that the optimum solution was within an acceptable error range. More precisely, the absolute percentage errors for Ra and Vbmax were 2.5 and 1.5, respectively. This work proposes an effective strategy for efficiently combining machine learning techniques and the NSGA-II multiobjective optimization algorithm. The experimental validations have reflected the potential for applying this strategy in various machining-optimization problems.