Deep Learning in Mechanical Metamaterials: From Prediction and Generation to Inverse Design

Advanced Materials, Год журнала: 2023, Номер 35(45)

Опубликована: Июнь 19, 2023

Abstract Mechanical metamaterials are meticulously designed structures with exceptional mechanical properties determined by their microstructures and constituent materials. Tailoring material geometric distribution unlocks the potential to achieve unprecedented bulk functions. However, current metamaterial design considerably relies on experienced designers' inspiration through trial error, while investigating responses entails time‐consuming testing or computationally expensive simulations. Nevertheless, recent advancements in deep learning have revolutionized process of metamaterials, enabling property prediction geometry generation without prior knowledge. Furthermore, generative models can transform conventional forward into inverse design. Many studies implementation highly specialized, pros cons may not be immediately evident. This critical review provides a comprehensive overview capabilities prediction, generation, metamaterials. Additionally, this highlights leveraging create universally applicable datasets, intelligently intelligence. article is expected valuable only researchers working but also those field materials informatics.

Язык: Английский

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Topology optimization for metal additive manufacturing: current trends, challenges, and future outlook

Virtual and Physical Prototyping, Год журнала: 2023, Номер 18(1)

Опубликована: Март 9, 2023

Metal additive manufacturing is gaining immense research attention. Some of these efforts are associated with physics, statistical, or artificial intelligence-driven process modelling and optimisation, structure–property characterisation, structural design equipment enhancements for cost reduction faster throughputs. In this review, the focus drawn on utilisation topology optimisation in metal manufacturing. First, symbiotic relationship between aerospace, medical, automotive, other industries investigated. Second, support structure by thermal-based powder-bed processes discussed. Third, introduction capabilities to limit constraints generate porous features examined. Fourth, emerging adopt intelligence models Finally, some open-source commercial software explored. This study considers challenges faced while providing perceptions future directions.

Язык: Английский

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Data‐Driven Design for Metamaterials and Multiscale Systems: A Review

Advanced Materials, Год журнала: 2023, Номер 36(8)

Опубликована: Дек. 5, 2023

Abstract Metamaterials are artificial materials designed to exhibit effective material parameters that go beyond those found in nature. Composed of unit cells with rich designability assembled into multiscale systems, they hold great promise for realizing next‐generation devices exceptional, often exotic, functionalities. However, the vast design space and intricate structure–property relationships pose significant challenges their design. A compelling paradigm could bring full potential metamaterials fruition is emerging: data‐driven This review provides a holistic overview this rapidly evolving field, emphasizing general methodology instead specific domains deployment contexts. Existing research organized modules, encompassing data acquisition, machine learning‐based cell design, optimization. The approaches further categorized within each module based on shared principles, analyze compare strengths applicability, explore connections between different identify open questions opportunities.

Язык: Английский

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Holistic computational design within additive manufacturing through topology optimization combined with multiphysics multi-scale materials and process modelling

Progress in Materials Science, Год журнала: 2023, Номер 138, С. 101129 - 101129

Опубликована: Апрель 23, 2023

Additive manufacturing (AM) processes have proven to be a perfect match for topology optimization (TO), as they are able realize sophisticated geometries in unique layer-by-layer manner. From viewpoint, however, there is significant likelihood of process-related defects within complex geometrical features designed by TO. This because TO seldomly accounts process constraints and conditions typically perceived purely design tool. On the other hand, advanced AM simulations shown their potential reliable tools capable predicting various hence serving second-to-none material tool achieving targeted properties. Thus far, these two geometry been traditionally viewed entirely separate paradigms, whereas one must conceive them holistic computational instead. More specifically, models provide input physics-based TO, where consequently, not only component will function optimally, but also near-to-minimum defects. In this regard, we aim at giving thorough overview concepts applied AM. The paper arranged following way: first, literature on performance reviewed then most recent developments techniques related covered. Process play pivotal role latter type serve additional top primary end-user objectives. As natural consequence this, comprehensive detailed review non-metallic metallic additive performed, divided into micro-scale deposition-scale simulations. Material multi-scaling which central process-structure-property relationships, next followed subsection reduced-order versions incorporable due lower requirements. Finally concluded suggestions further research paths discussed.

Язык: Английский

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Rational designs of mechanical metamaterials: Formulations, architectures, tessellations and prospects

Materials Science and Engineering R Reports, Год журнала: 2023, Номер 156, С. 100755 - 100755

Опубликована: Окт. 7, 2023

Язык: Английский

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Denoising diffusion algorithm for inverse design of microstructures with fine-tuned nonlinear material properties

Computer Methods in Applied Mechanics and Engineering, Год журнала: 2023, Номер 413, С. 116126 - 116126

Опубликована: Июнь 2, 2023

Язык: Английский

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Photocatalytic decomposition of metronidazole by zinc hexaferrite coated with bismuth oxyiodide magnetic nanocomposite: Advanced modelling and optimization with artificial neural network

Chemosphere, Год журнала: 2024, Номер 356, С. 141770 - 141770

Опубликована: Март 28, 2024

Язык: Английский

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Deep learning in computational mechanics: a review

Computational Mechanics, Год журнала: 2024, Номер 74(2), С. 281 - 331

Опубликована: Янв. 13, 2024

Abstract The rapid growth of deep learning research, including within the field computational mechanics, has resulted in an extensive and diverse body literature. To help researchers identify key concepts promising methodologies this field, we provide overview deterministic mechanics. Five main categories are identified explored: simulation substitution, enhancement, discretizations as neural networks, generative approaches, reinforcement learning. This review focuses on methods rather than applications for thereby enabling to explore more effectively. As such, is not necessarily aimed at with knowledge learning—instead, primary audience verge entering or those attempting gain discussed are, therefore, explained simple possible.

Язык: Английский

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A novel interval dynamic topology optimization methodology of piezoelectric structures under reliable active control

Computer Methods in Applied Mechanics and Engineering, Год журнала: 2024, Номер 421, С. 116766 - 116766

Опубликована: Янв. 17, 2024

Язык: Английский

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On benchmarking and good scientific practise in topology optimization

Structural and Multidisciplinary Optimization, Год журнала: 2022, Номер 65(11)

Опубликована: Окт. 25, 2022

Язык: Английский

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