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A machine learning workflow for 4D printing: understand and predict morphing behaviors of printed active structures

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This article is published in Smart Materials and Structures.The article was published on 2021-01-01. It has received 18 citations till now. The article focuses on the topics: Workflow & Morphing.

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Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment

TL;DR: In this article, a new approach to design hierarchical materials using machine learning, trained with a database of hundreds of thousands of structures from finite element analysis, together with a self-learning algorithm for discovering high-performing materials where inferior designs are phased out for superior candidates.
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Soft Pneumatic Actuators: A Review of Design, Fabrication, Modeling, Sensing, Control and Applications

TL;DR: In this article , the development of soft pneumatic actuators and robots up to the date of publication is summarized and a special emphasis on recent advances such as novel designs, differential simulators, analytical and numerical modeling methods, topology optimization, data-driven modeling and control methods, hardware control boards and nonlinear estimation and control techniques.
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4D printing in biomedical applications: emerging trends and technologies

TL;DR: In this paper, the authors report the recent progress in the design and development of smart materials that are actuated by different stimuli and their exploitation within additive manufacturing to produce biomimetic structures with important repercussions in different but interrelated biomedical areas.
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Mechanomaterials: A Rational Deployment of Forces and Geometries in Programming Functional Materials.

TL;DR: The field of functional mechanomaterials is still in its infancy, with many open challenges and opportunities that need to be addressed as mentioned in this paper, and a variety of techniques have been recruited to deliver the multiscale programming of functional materials, such as strain engineering, capillary assembly, topological interlocking, kirigami, origami, to name a few.
Journal ArticleDOI

Toward enabling manufacturing paradigm of 4D printing of Shape Memory Materials: Open literature review

TL;DR: In this paper , the shape memory properties of shape memory polymers (SMPs), SM-gels (SMGs), and SM-alloys (SMAs) from design-modeling for 4D printing are identified, and corresponding indications are outlined.
References
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Proceedings ArticleDOI

XGBoost: A Scalable Tree Boosting System

TL;DR: XGBoost as discussed by the authors proposes a sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning to achieve state-of-the-art results on many machine learning challenges.
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Machine learning for molecular and materials science.

TL;DR: A future in which the design, synthesis, characterization and application of molecules and materials is accelerated by artificial intelligence is envisaged.
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Biomimetic 4D printing

TL;DR: In this article, a plant-inspired shape morphing system is presented, where a composite hydrogel architecture is encoded with localized, anisotropic swelling behavior controlled by the alignment of cellulose fibrils along prescribed four-dimensional printing pathways.
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3-D printing: The new industrial revolution

TL;DR: The authors examines the characteristics and applications of 3D printing and compares it with mass customization and other manufacturing processes, and concludes that 3-D printing enables small quantities of customized goods to be produced at relatively low costs.
Journal ArticleDOI

Printing ferromagnetic domains for untethered fast-transforming soft materials

TL;DR: 3D printing of programmed ferromagnetic domains in soft materials that enable fast transformations between complex 3D shapes via magnetic actuation are reported, enabling a set of previously inaccessible modes of transformation, such as remotely controlled auxetic behaviours of mechanical metamaterials with negative Poisson’s ratios.