Abstract
The manufacture of large wind turbine blades requires well-controlled processing conditions to prevent defect formation and thus produce high-quality composite blades. While the physics-based models provide accurate computational capabilities for the resin infusion and curing process for the glass fiber composites, they suffer from high computational costs, making them infeasible for fast optimization computation and process control during manufacturing. In light of the limitations, we describe a machine learning (ML) approach that employs a deep convolutional and recurrent neural network model to predict the spatio-temporal temperature distribution during the vacuum assisted resin infusion molding (VARIM) process. The ML model is trained with the “big data” generated from the physics-based high-fidelity simulations. Once fully trained, it serves as a digital twin of the blade manufacturing process. Validation is made by comparing simulation results with experimental data on a unidirectional glass fiber composite laminate plate (44 plies, 2 m long and 0.5 m wide). The trained and validated ML model is then extended to evaluate the role of critical VARIM processing parameters on temperature distribution. With the predictive accuracy of 94%, at over 100 times faster computational speed than the physics-based simulations, the ML approach established herein provides a general framework for a digital twin for temperature distribution in the composite manufacturing process.
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Datasets, codes, and models used in this paper have been uploaded to GitHub as a reference for readers interested in performing additional analysis. Weblink to GitHub: https://github.com/Runyu-Zhang/Spatio-temporal-temperature-prediction-for-VARIM-process
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Acknowledgements
This paper is based upon work partially supported by the National Science Foundation under Grant Numbers 1362033 and 1916776 (I/UCRC for Wind Energy, Science, Technology, and Research) and from the members of WindSTAR I/UCRC. We also acknowledge the support of the Department of Energy, under Award Numbers DE-NA0003962 and DE-NA-0003525. We acknowledge Paul Ubrich, Mirna Robles, Nathan Bruno at Westlake Epoxy for helpful discussions. Lu also acknowledges the Louis A. Beecherl Jr. Chair for additional support. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation or the Department of Energy, or the sponsors. TPI Composites, Inc. is a composite manufacturer with a sector of business in the manufacturing of wind turbine blades, and a sponsoring company in the National Science Foundation (NSF) WindSTAR I/UCRC Center jointly operated between the University of Massachusetts at Lowell, and the University of Texas at Dallas. Olin™ EPOXY is a supplier and manufacturer of epoxy products, and a sponsoring company in the National Science Foundation (NSF) WindSTAR I/UCRC Center jointly operated between the University of Massachusetts at Lowell, and the University of Texas at Dallas. The partial support of UTD Wind is gratefully acknowledged.
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Zhang, R., Liu, Y., Zheng, T. et al. A fast spatio-temporal temperature predictor for vacuum assisted resin infusion molding process based on deep machine learning modeling. J Intell Manuf 35, 1737–1764 (2024). https://doi.org/10.1007/s10845-023-02113-4
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DOI: https://doi.org/10.1007/s10845-023-02113-4