A Thermal Displacement Prediction System with an Automatic LRGTVAC-PSO Optimized Branch Structured Bidirectional GRU Neural Network

• Published in: IEEE sensors journal Vol. 23; no. 12; p. 1
• Main Authors: Kuo, Ping-Huan, Chen, Yen-Wen, Hsieh, Tung-Hsien, Jywe, Wen-Yuh, Yau, Her-Terng
• Format: Journal Article
• Language: English
• Published: New York IEEE 15.06.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Auto Optimization; CNC machine tools; Data models; Errors; GRU; LSTM; Machine learning; Machine learning algorithms; Machine tools; Machining; Manufacturing; Neural networks; Particle swarm optimization; Prediction algorithms; Production methods; PSO; Temperature measurement; Temperature sensors; Thermal displacement
• DOI: 10.1109/JSEN.2023.3269064
• ISSN: 1530-437X

Considering technology's rapid development, traditional manufacturing methods are insufficient to achieve the high accuracy demanded by aerospace, national defense, and numerous leading-edge engineering projects. Thermal displacement is a significant source of manufacturing errors, and accurately correcting such errors is difficult or even impossible using traditional machining methods. This paper proposes a machine learning method for high-accuracy error prediction that nonprofessionals can easily implement. An optimized automatic Logistic Random Generator Time Varying Acceleration Coefficient Particle Swarm Optimization (LRGTVAC-PSO) method is proposed to optimize a branch structured bidirectional Gated Recurrent Unit (GRU) neural network. The accuracy of the proposed method (with a three-axis average of 0.945) is superior to that of the other optimized algorithms evaluated in this study. The method serves as a means not only of accurately predicting thermal displacement but also of autotuning the hyperparameters of machine learning algorithms.