Application of deep neural network learning in composites design
Application of deep neural network learning in composites design
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DOI：
10.1080/26889277.2022.2053302
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发表时间：
2022-03
期刊：
影响因子：
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通讯作者：
Yinli Wang;Constantinos Soutis;D. Ando;Y. Sutou;F. Narita
中科院分区：
文献类型：
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作者：
Yinli Wang;Constantinos Soutis;D. Ando;Y. Sutou;F. Narita
摘要对人工智能（AI）进行了及时的回顾，更具体地说，深度学习是机器学习（ML）的一个子领域，应用于现代复合材料系统的设计和行为。复合材料的使用正在增加，这是由于它们的高比强度和刚度，使它们与金属相当，以及它们的可调特性，可以改变以生产具有有效结构配置的轻质材料。最近的研究进行了检查和讨论，其中已经开发了模拟人脑活动的计算工具，以回答问题和解决具有挑战性的问题，以表征材料的行为，并以更少的努力和成本提高材料的性能。人工智能的吸引力来自于它的自我学习能力，更快的计算机处理大型数据集的时间，以及产生高度准确结果的潜力。然而，作为一种数据驱动的方法，数据的数量和质量在很大程度上影响了ML的准确性，此外还需要设计良好的AI算法和虚拟现实模型，因此需要继续在这一领域进行研究。
Abstract A timely review is presented on artificial intelligence (AI) and, more specifically, deep learning, which is a subfield of machine learning (ML), applied to the design and behaviour of modern composite materials systems. The use of composites is increasing due to their high specific strength and stiffness, which make them comparable to metals, and their tunable properties that can be altered to produce lightweight materials with efficient structural configurations. Recent studies are examined and discussed, wherein computational tools have been developed that mimic human brain activity to answer questions and solve challenging problems toward characterizing materials behaviour and improving the performance of materials with less effort and cost. The attractiveness of AI comes from its self-learning capability, the faster computer processing time of large datasets, and the potential to yield highly accurate results. However, as a data-driven method, the quantity and quality of data largely affect the accuracy of ML in addition to the need for well-designed AI algorithms and virtual reality models, hence the need to continue the research efforts in this area.