This study presents surface roughness modeling for machined parts based on cutting parameters (spindle speed, cutting depth, and feed rate) and machining vibration in the end milling process. Prediction models were developed using multiple regression analysis and an artificial neural network (ANN) modeling approach. To reduce the effect of chatter, machining tests were conducted under varying cutting parameters as defined in the stable regions of the milling tool. The surface roughness and machining vibration level are modeled with nonlinear quadratic forms based on the cutting parameters and their interactions through multiple regression analysis methods, respectively. Analysis of variance was employed to determine the significance of cutting parameters on surface roughness. The results show that the combined effects of spindle speed and cutting depth significantly influence surface roughness. The comparison between the prediction performance of the multiple regression and neural network-based models reveal that the ANN models achieve higher prediction accuracy for all training data with R = 0.96 and root mean square error (RMSE) = 3.0% compared with regression models with R = 0.82 and RMSE = 7.57%. Independent machining tests were conducted to validate the predictive models; the results conclude that the ANN model based on cutting parameters with machining vibration has a higher average prediction accuracy (93.14%) than those of models with three cutting parameters. Finally, the feasibility of the predictive model as the base to develop an online surface roughness recognition system has been successfully demonstrated based on contour surface milling test. This study reveals that the predictive models derived on the cutting conditions with consideration of machining stability can ensure the prediction accuracy for application in milling process.

中文翻译：

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基于回归参数和人工神经网络的立铣刀切削参数和加工振动的表面粗糙度预测

这项研究提出了基于切削参数（主轴速度，切削深度和进给速度）以及端铣削过程中的加工振动的机械零件表面粗糙度建模。使用多元回归分析和人工神经网络（ANN）建模方法开发了预测模型。为了减少颤动的影响，在铣刀的稳定区域中定义的各种切削参数下进行了机加工测试。根据切削参数及其相互作用，通过多重回归分析方法分别用非线性二次形式对表面粗糙度和加工振动水平进行建模。采用方差分析来确定切削参数对表面粗糙度的重要性。结果表明，主轴转速和切削深度的综合影响显着影响表面粗糙度。多元回归模型和基于神经网络的模型的预测性能之间的比较表明，与具有R的回归模型相比，ANN模型对R = 0.96和均方根误差（RMSE）= 3.0％的所有训练数据均具有更高的预测精度。 = 0.82，RMSE = 7.57％。进行了独立的机加工测试以验证预测模型；结果表明，基于切削参数和加工振动的人工神经网络模型比具有三个切削参数的模型具有更高的平均预测精度（93.14％）。最后，基于轮廓铣削试验成功地证明了预测模型作为开发在线表面粗糙度识别系统基础的可行性。这项研究表明，在考虑切削稳定性的情况下基于切削条件得出的预测模型可以确保在铣削过程中应用的预测精度。