A Thermal Displacement Prediction System with an Automatic LRGTVAC-PSO Optimized Branch Structured Bidirectional GRU Neural Network - Kuo, Ping-Huan; Chen, Yen-Wen; Hsieh, Tung-Hsien; Jywe, Wen-Yuh; Yau, Her-Terng (IEEE sensors journal, 2023)
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.