applied sciences Article Prediction of Surface Roughness Based on Cutting Parameters and Machining Vibration in End Milling Using Regression Method and Artificial Neural Network Yung-Chih Lin 1,2, Kung-Da Wu 1, Wei-Cheng Shih 1, Pao-Kai Hsu 1 and Jui-Pin Hung 1,* 1 Graduate Institute of Precision Manufacturing, National Chin-Yi University of Technology, Taichung 41170, Taiwan;
[email protected] (Y.-C.L.); ffi
[email protected] (K.-D.W.);
[email protected] (W.-C.S.);
[email protected] (P.-K.H.) 2 Intelligent Machinery Technology Center, Industrial Technology Research Institute, Taichung 40852, Taiwan * Correspondence:
[email protected]; Tel.: +88-6423-924-505 (ext. 7181); Fax: +88-6423-925-714 Received: 14 May 2020; Accepted: 3 June 2020; Published: 5 June 2020 Abstract: 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%.