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

Wonbong Choi; Rigoberto C. Advincula; H. Felix Wu; Yijie Jiang

doi:10.1557/s43579-023-00473-9

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

Wonbong Choi, Rigoberto C. Advincula, H. Felix Wu, Yijie Jiang

Macromolecular Nanomaterials

Research output: Contribution to journal › Article › peer-review

11 Scopus citations

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. Graphical abstract: [Figure not available: see fulltext.]

Original language	English
Pages (from-to)	714-724
Number of pages	11
Journal	MRS Communications
Volume	13
Issue number	5
DOIs	https://doi.org/10.1557/s43579-023-00473-9
State	Published - Oct 2023

Funding

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].

Funders	Funder number
U.S. Department of Energy	VTO CPS 36928

Keywords

Additive manufacturing
Artificial intelligence
Composite
Machine learning
Microstructure

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1557/s43579-023-00473-9

Cite this

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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. Graphical abstract: [Figure not available: see fulltext.]",

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AB - 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. Graphical abstract: [Figure not available: see fulltext.]

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

Abstract

Funding

Keywords

UN SDGs

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