A fast spatio-temporal temperature predictor for vacuum assisted resin infusion molding process based on deep machine learning modeling
A fast spatio-temporal temperature predictor for vacuum assisted resin infusion molding process based on deep machine learning modeling
复制标题
基于深度机器学习建模的真空辅助树脂灌注成型过程的快速时空温度预测器
DOI：
10.1007/s10845-023-02113-4
复制
发表时间：
2023
影响因子：
8.3
通讯作者：
Qian, Dong
中科院分区：
文献类型：
--
作者：
Zhang, Runyu;Liu, Yingjian;Zheng, Thomas;Eddin, Sarah;Nolet, Steven;Liang, Yi-Ling;Rezazadeh, Shaghayegh;Wilson, Joseph;Lu, Hongbing;Qian, Dong
大型风力涡轮机叶片的制造需要良好控制的加工条件，以防止缺陷形成，从而生产出高质量的复合材料叶片。虽然基于物理的模型为玻璃纤维复合材料的树脂灌注和固化过程提供了精确的计算能力，但它们的计算成本很高，使得它们无法在制造过程中进行快速优化计算和过程控制。鉴于这些限制，我们描述了一种机器学习（ML）方法，该方法采用深度卷积和循环神经网络模型来预测真空辅助树脂灌注成型（VARIM）过程中的时空温度分布。机器学习模型使用基于物理的高保真模拟生成的“大数据”进行训练。一旦经过充分培训，它就可以作为叶片制造过程的数字孪生。通过将模拟结果与单向玻璃纤维复合层压板（44 层，长 2 m，宽 0.5 m）的实验数据进行比较来进行验证。经过训练和验证的 ML 模型随后被扩展，以评估关键 VARIM 处理参数对温度分布的作用。本文建立的机器学习方法的预测精度为 94%，计算速度比基于物理的模拟快 100 倍以上，为复合材料制造过程中温度分布的数字孪生提供了通用框架。
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.
影响因子：
64.8
作者：
Jumper J;Evans R;Pritzel A;Green T;Figurnov M;Ronneberger O;Tunyasuvunakool K;Bates R;Žídek A;Potapenko A;Bridgland A;Meyer C;Kohl SAA;Ballard AJ;Cowie A;Romera-Paredes B;Nikolov S;Jain R;Adler J;Back T;Petersen S;Reiman D;Clancy E;Zielinski M;Steinegger M;Pacholska M;Berghammer T;Bodenstein S;Silver D;Vinyals O;Senior AW;Kavukcuoglu K;Kohli P;Hassabis D
通讯作者：
Hassabis D
使用深度强化学习在液体复合材料成型中进行即时流量分配网络优化
影响因子：
8.3
作者：
Martin Szarski;S. Chauhan
通讯作者：
S. Chauhan
通过安全外壳通过无线局域网实时收集热电偶数据的方法。
影响因子：
1.6
作者：
F. Arnold;I. DeMallie;L. Florence;D. Kashinski
通讯作者：
D. Kashinski