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

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 accurate...

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Vydáno v:IEEE sensors journal Ročník 23; číslo 12; s. 1
Hlavní autoři: Kuo, Ping-Huan, Chen, Yen-Wen, Hsieh, Tung-Hsien, Jywe, Wen-Yuh, Yau, Her-Terng
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 15.06.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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
ISSN:1530-437X, 1558-1748
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Shrnutí: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.
Bibliografie:ObjectType-Article-1
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ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2023.3269064