Kuo, Ping HuanPing HuanKuoChen, Yen WenYen WenChenHsieh, Tung HsienTung HsienHsiehWen-Yuh JyweYau, Her TerngHer TerngYau2023-06-092023-06-092023-01-011530437Xhttps://scholars.lib.ntu.edu.tw/handle/123456789/632038Considering 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.Auto Optimization | CNC machine tools | GRU | LSTM | machine learning | PSO | Thermal displacement[SDGs]SDG9journal article10.1109/JSEN.2023.32690642-s2.0-85159667711https://api.elsevier.com/content/abstract/scopus_id/85159667711