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Artificial intelligence and machine learning in the design and additive manufacturing of responsive composites

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Abstract

In recent years, the development of artificial intelligence (AI) and machine learning (ML) techniques has revolutionized composite design. Researchers have investigated intricate structures with tailored properties and dynamic responsive behaviors by leveraging additive manufacturing (AM) methods, such as AI-guided 3D printing and 4D printing. This approach accelerates simulations, optimizes material selection, design of new structures with multi functionalities, and reduces time and costs. AI/ML techniques offer powerful tools for advancing the designs of high-performance composites and innovative functional materials. Here, a summary of current AI/ML guided designs of digital materials composites and responsive materials is provided, and a discussion of opportunities and challenges to further advance in this area is followed.

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Acknowledgments

The authors acknowledge the funding support by the Vehicle Technologies Office (VTO) in the U.S. Department of Energy (DOE) [grant number: VTO CPS 36928].

Funding

Vehicle Technologies Office,VTO CPS 36928,Wonbong Choi,VTO CPS 36928,Rigoberto C. Advincula,VTO CPS 36928, Yijie Jiang

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Authors and Affiliations

  1. Department of Mechanical Engineering, University of North Texas, Denton, TX, 76207, USA

    Wonbong Choi & Yijie Jiang

  2. Department of Materials Science and Engineering, University of North Texas, Denton, TX, 76207, USA

    Wonbong Choi

  3. Center for Nanophase Materials and Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37830, USA

    Rigoberto C. Advincula

  4. Department of Chemical and Biomolecular Engineering, University of Tennessee, Knoxville, TN, 37996, USA

    Rigoberto C. Advincula

  5. Vehicle Technologies Office, U.S. Department of Energy, 1000 Independence Avenue, SW, Washington, D.C, 20585, USA

    H. Felix Wu

  6. School of Aerospace and Mechanical Engineering, University of Oklahoma, Norman, OK, 73019, USA

    Yijie Jiang

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  1. Wonbong Choi
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All authors contributed to the manuscript writing and revision. All authors read and approved the final manuscript.

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Correspondence to Yijie Jiang.

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Choi, W., Advincula, R.C., Wu, H.F. et al. Artificial intelligence and machine learning in the design and additive manufacturing of responsive composites. MRS Communications 13, 714–724 (2023). https://doi.org/10.1557/s43579-023-00473-9

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