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 turning—namely, feed rate, depth of cut, spindle speed, and vibration of the X-, Y-, and Z-axis—are the main factors affecting surface quality. In this research, six machine learning- (ML-) based models—artificial 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.

中文翻译：

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应用贝叶斯优化机器学习模型预测单点金刚石车削聚碳酸酯的表面粗糙度

本文通过对机器学习模型应用贝叶斯优化来预测制造聚碳酸酯 (PC) 的表面粗糙度。超精密的输入变量转弯即，进给速度，切削，主轴转速的深度和振动X-，Y-，和ž-axis——是影响表面质量的主要因素。在这项研究中，六种基于机器学习 (ML-) 的模型——人工神经网络 (ANN)、Cat Boost 回归 (CAT)、支持向量机 (SVR)、梯度提升回归 (GBR)、决策树回归 (DTR)、和极端梯度提升回归（XGB）——用于预测表面粗糙度（Ra）。基线模型的预测性能通过误差指标进行定量评估：均方根误差 (RMSE)、平均绝对误差 (MAE) 和决定系数 ( R ²）。总体结果表明，XGB 和 CAT 模型预测 Ra 的准确度最高。在改进 XGB 和 CAT 等基线模型时，接下来使用贝叶斯优化 (BO) 来确定它们的最佳超参数，结果表明根据评估指标 XGB 是最佳模型。结果表明，使用 BO 后模型的性能得到了显着提高。例如，对于训练数据集，XGB 的 RMSE 和 MAE 值分别从 0.0076 下降到 0.0047 和从 0.0063 下降到 0.0027。使用测试数据集，XGB 的 RMSE 和 MAE 值分别从 0.4033 下降到 0.2512 和从 0.2845 下降到 0.2225。此外，X、Y和Z的振动轴和进给速度是预测结果的最显着特征，与文献高度一致。我们发现，在指定值域中，轴的振动对表面质量的影响大于切削条件。