Thermal positioning error modeling of machine tools using a bat algorithm-based back propagation neural network

• Published in: International journal of advanced manufacturing technology Vol. 97; no. 5-8; pp. 2575 - 2586
• Main Authors: Li, Yang, Zhao, Ji, Ji, Shijun
• Format: Journal Article
• Language: English
• Published: London Springer London 01.07.2018; Springer Nature B.V
• Subjects: Algorithms; Back propagation; Back propagation algorithms; Back propagation networks; CAE) and Design; Computer-Aided Engineering (CAD; Engineering; Errors; Industrial and Production Engineering; Machine tool industry; Machine tools; Machining; Mathematical models; Mechanical Engineering; Media Management; Modelling; Neural networks; Optimization; Original Article; Positioning devices (machinery); Propagation; Three axis; Thermal error modeling; BP neural network; Bat algorithm; Swarm intelligent optimization algorithm; CNC machine tools
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-018-1978-x

Thermal error of a machine tool is one of the main reasons affecting the machining accuracy. Heat production and heat transfer of a machine tool are too complicated to predict the generated thermal error accurately. According to the nonlinear and time-varying characteristics of thermal error, the back propagation (BP) neural network is perfectly suitable for thermal error modeling, which has been extensively used to map the nonlinear relationship. However, traditional BP neural network usually has poor prediction performance under different operating conditions. Therefore, a new swarm intelligent optimization algorithm, bat algorithm (BA), is introduced to optimize BP neural network and improve its performance. The focus of this paper is the application of the combined algorithm (bat algorithm-based back propagation neural network) to solve the problem of thermal error modeling. Thermal positioning error experiments were conducted on a three-axis experiment bench. The experimental results show that thermal positioning error model built by BA-BP neural network is more stable and has high prediction accuracy and strong robustness, which can provide a candidate method for thermal error modeling.