Precision Obtained Using an Artificial Neural Network for Predicting the Material Removal Rate in Ultrasonic Machining
College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiao tong University, Xi’an 710054, China
Production engineering, Machine design and Automation (PMA), Department of Mechanical Engineering, Katholieke Universiteit Leuven (KU Leuven), 3001 Leuven, Belgium
Faculty of Engineering and the Environment, University of Southampton, Southampton SO17 1BJ, UK
Author to whom correspondence should be addressed.
Appl. Sci. 2017, 7(12), 1268; https://doi.org/10.3390/app7121268
Received: 15 September 2017 / Revised: 27 October 2017 / Accepted: 8 November 2017 / Published: 5 December 2017
(This article belongs to the Section Mechanical Engineering)
The present study proposes a back propagation artificial neural network (BPANN) to provide improved precision for predicting the material removal rate (MRR) in ultrasonic machining. The BPANN benefits from the advantage of artificial neural networks (ANNs) in dealing with complex input-output relationships without explicit mathematical functions. In our previous study, a conventional linear regression model and improved nonlinear regression model were established for modelling the MRR in ultrasonic machining to reflect the influence of machining parameters on process response. In the present work, we quantitatively compare the prediction precision obtained by the previously proposed regression models and the presently proposed BPANN model. The results of detailed analyses indicate that the BPANN model provided the highest prediction precision of the three models considered. The present work makes a positive contribution to expanding the applications of ANNs and can be considered as a guide for modelling complex problems of general machining.