Application of deep neural network learning in composites design. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Wang, Yinli; Soutis, Constantinos; Ando, Daisuke; Sutou, Yuji; Narita, Fumio
• Is Part Of: European Journal of Materials. Volume 2:Issue 1(2022)
• Page Start: 118
• Page End: 171
• Publication Date: 2022-12-31
• Subjects: Material design -- topology optimization -- mapping -- neural networks -- material databases -- damage analysis -- multiscale modelling -- material property prediction -- industrial internet of things -- Industry 4.0; 620.11
• DOI: 10.1080/26889277.2022.2053302
• Languages: English
• ISSNs: 2688-9277

Abstract

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.