A fast spatio-temporal temperature predictor for vacuum assisted resin infusion molding process based on deep machine learning modeling

Runyu Zhang, Yingjian Liu, Thomas Zheng, Sarah Eddin, Steven Nolet, Yi-Ling Liang, Shaghayegh Rezazadeh, Joseph Wilson, Hongbing Lu and Dong Qian ()
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Runyu Zhang: The University of Texas at Dallas
Yingjian Liu: The University of Texas at Dallas
Thomas Zheng: Texas A&M University
Sarah Eddin: The University of North Carolina at Charlotte
Steven Nolet: TPI Composites, Inc.
Yi-Ling Liang: Olin™ EPOXY
Shaghayegh Rezazadeh: TPI Composites, Inc.
Joseph Wilson: TPI Composites, Inc.
Hongbing Lu: The University of Texas at Dallas
Dong Qian: The University of Texas at Dallas

Journal of Intelligent Manufacturing, 2024, vol. 35, issue 4, No 17, 1737-1764

Abstract: 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.

Keywords: Vacuum assisted resin infusion molding (VARIM); Machine learning (ML); Deep convolutional neural network (CNN); Recurrent neural network (RNN); Long short-term memory (LSTM); Physics-informed surrogate model (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10845-023-02113-4

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