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Open AccessArticle

Prediction of Surface Roughness Based on Cutting Parameters and Machining Vibration in End Milling Using Regression Method and Artificial Neural Network

1
Graduate Institute of Precision Manufacturing, National Chin-Yi University of Technology, Taichung 41170, Taiwan
2
Intelligent Machinery Technology Center, Industrial Technology Research Institute, Taichung 40852, Taiwan
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(11), 3941; https://doi.org/10.3390/app10113941
Received: 14 May 2020 / Revised: 1 June 2020 / Accepted: 3 June 2020 / Published: 5 June 2020
(This article belongs to the Section Mechanical Engineering)
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. View Full-Text
Keywords: artificial neural network; cutting parameters; machining vibration; regression analysis; surface roughness artificial neural network; cutting parameters; machining vibration; regression analysis; surface roughness
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MDPI and ACS Style

Lin, Y.-C.; Wu, K.-D.; Shih, W.-C.; Hsu, P.-K.; Hung, J.-P. Prediction of Surface Roughness Based on Cutting Parameters and Machining Vibration in End Milling Using Regression Method and Artificial Neural Network. Appl. Sci. 2020, 10, 3941. https://doi.org/10.3390/app10113941

AMA Style

Lin Y-C, Wu K-D, Shih W-C, Hsu P-K, Hung J-P. Prediction of Surface Roughness Based on Cutting Parameters and Machining Vibration in End Milling Using Regression Method and Artificial Neural Network. Applied Sciences. 2020; 10(11):3941. https://doi.org/10.3390/app10113941

Chicago/Turabian Style

Lin, Yung-Chih; Wu, Kung-Da; Shih, Wei-Cheng; Hsu, Pao-Kai; Hung, Jui-Pin. 2020. "Prediction of Surface Roughness Based on Cutting Parameters and Machining Vibration in End Milling Using Regression Method and Artificial Neural Network" Appl. Sci. 10, no. 11: 3941. https://doi.org/10.3390/app10113941

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