Machine-learning based design of active composite structures for 4D printing
Open accessAbstract
Abstract Active composites are a class of materials that have environmentally responsive components within them. One key advantage of active composites is that through mechanics design, a variety of actuation can be achieved. The development of active composites has been significantly enhanced in recent years by multimaterial 3D printing where different materials can be precisely placed in 3D space, enabling the achievement of shape-shifting of 3D printed parts, or 4D printing. In practical applications, it is highly desirable that the part shape can change in a pre-described manner, which requires the careful design of where to place different materials. However, designing an active composite structure to achieve a target shape change is challenging because it requires solving an inverse problem with spatially heterogenous, highly nonlinear (active) material behavior within a potentially complex boundary value problem. In this paper we present a machine learning approach to the design of active composite structures that can achieve target shape shifting responses. Our strategy is to combine the finite element method with an evolutionary algorithm. In order to achieve a target shape, we compose the structures of equally sized voxel units that are made of either a passive or an active material and optimize the distribution of these two material phases. The optimization method is tested against several illustrative examples in active composite design to show the agreement between the target shape and the best machine learning solution obtained.
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A recent machine-learning approach has been proposed for the design of active composite structures.
“Finally, it worth recalling that a recent approach based on machine-learning has been proposed in [288] for the design of active composite structures.”
From Two-Way and Multiple-Way Shape Memory Polymers for Soft Robotics: An Overview (2020) · cites this paper as [288]
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- Authors
- Hamel, C. M., Roach, D. J., Long, K. N., Demoly, F., Dunn, M. L., & Qi, H. J.
- Affiliations
- Georgia Institute of Technology (US)
- Sandia National Laboratories (US)
- Centre National de la Recherche Scientifique (FR)
- University of Colorado Denver (US)
- Corresponding author
- Qi, H Jerry
- Venue
- Smart Materials and Structures
- Publisher
- IOP Publishing
- Published
- 2019-05-01
- Volume
- 28
- Issue
- 6
- Pages
- 065005
- ISSN
- 0964-1726 (ISSN-L)
- 1361-665X
- OpenAlex
- W2933287546
- Cited by
- 159
- References
- 38
- Citations by year
- 2026: 14 · 2025: 25 · 2024: 27 · 2023: 27 · 2022: 28 · 2021: 16 · 2020: 19 · 2019: 3
- FWCI
- 9.28
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Smart Materials and Structures (publishedVersion)
- HAL (submittedVersion)
- Funders
- Air Force Office of Scientific Research (FA9550-16-1-0169)
- Topics
- Physical Sciences › Engineering › Mechanical Engineering › Advanced Materials and Mechanics
- Physical Sciences › Engineering › Civil and Structural Engineering › Topology Optimization in Engineering
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Keywords
- active composites, 4D printing, shape-shifting, inverse design, evolutionary algorithms, machine learning, multi-material printing, actuation, shape-changing structures
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