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An Accelerated Process Optimization Method to Minimize Deformations in Composites Using Theory-guided Probabilistic Machine Learning
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DOI：10.1016/j.compositesa.2023.107842.png)
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摘要
En 中文
While aerospace manufacturing and assembly technologies have significantly evolved, challenges persist in mitigating process-induced deformations (PIDs) in composite parts. Physics-based-simulation optimization strategies have been developed to address these challenges. However, they often prove ineffective or inefficient because of the time-consuming, costly, and deterministic characterization efforts they require. This study introduces a novel characterization- and simulation-free optimization approach for predicting and minimizing PIDs in composite parts. The method uses a theory-guided machine learning (TGML) framework, consisting of limited element-level experiments and Gaussian Process Regression (GPR) with integrated closed-form domain knowledge. This paper showcases the effectiveness of the proposed TGML method for two case studies, where an optimal layup and cure cycle are found and validated after a mere six experiments and without any material characterization or simulation efforts. The method presented in this paper aims to offer a cost-efficient and generalizable process optimization procedure to potentially mitigate PIDs in composite parts.
Keyword:
A. Polymer-matrix composites (PMCs)
B. Residual/internal stress
C. Statistical properties/methods
E. Autoclave
OTHER. Theory-guided machine learning
Process-induced deformations (PIDs)
期刊
IF:
8.9
论文数:
8.8K
被引数:
4.5W
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引用论文
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OAAI分享收藏Multiscale characterization and representation of composite materials during processing.png)
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCESIF3.7
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