Accelerated design of architectured ceramics with tunable thermal resistance via a hybrid machine learning and finite element approach

Fatehi, Erfan; Sarvestani, Hamidreza Yazdani; Ashrafi, Behnam; Akbarzadeh, A.H.

doi:10.1016/j.matdes.2021.110056

Cited by 30 publications

(20 citation statements)

References 47 publications

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“…This precise and varying the size and angle over the panel width. The panels developed here can be used for armor applications (such as personnel protective equipment) [26] or for thermal barrier systems [27].…”

Section: Discussionmentioning

confidence: 99%

Architectured Ceramics: Interlocking Design, Programmable Laser Manufacturing and Testing

Sarvestani

Esmail

Katz

et al. 2022

Preprint

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Tough and impact-resistant ceramic systems offer a wide range of remarkable opportunities beyond those offered by conventional brittle ceramics. However, despite their promise, the availability of traditional manufacturing techniques to fabricate such advanced ceramic structures in a highly controllable and scalable manner poses a significant manufacturing bottleneck. In this study, a precise and programmable laser manufacturing system was used to manufacture architectured ceramics inspired by biological materials such as bone, nacre and tooth enamel. This manufacturing strategy offers feasible mechanisms for precise material architecture and quantitative process control, particularly when scalability is considered. An optimized material removal method that approaches near-net shaping was employed to fabricate topologically interlocking ceramic systems (load-carrying assemblies of building blocks interacting by contact and friction) with different architectures (i.e., interlocking angle and building block size) subjected to low-velocity impact conditions. These impacts were evaluated using 3D digital image correlation (DIC). The optimal architectured ceramics exhibited a higher deformation (up to 310%) than the others. Their performance was tuned by controlling the interlocking angle and block size, adjusting the frictional sliding, and minimizing damage to the building blocks. The developed subtractive manufacturing technique leads to the fabrication of tough, impact-resistant, damagetolerant ceramic systems with excellent versatility and potential scalability.

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Section: Discussionmentioning

confidence: 99%

Architectured Ceramics: Interlocking Design, Programmable Laser Manufacturing and Testing

Sarvestani

Esmail

Katz

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The hybrid model of DL and global optimization algorithms (Bayesian optimization and GA) can well solve the problem that DL models require a large number of data sets, and efficiently and accurately reverse the material composition [127]. Using a hybrid finite element algorithm and feedforward neural network model, it is possible to design and design high-performance structural ceramics that experience thermal load [128]. In a hybrid model composed of DNNs and migration learning algorithms, migration learning is used to solve typical small data collection problems in materials.…”

Section: Hybrid Models With Other Algorithmsmentioning

confidence: 99%

Deep learning modeling strategy for material science: from natural materials to metamaterials

Chen

Xiong

et al. 2022

J. Phys. Mater.

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Computational modeling is a crucial approach in material-related research for discovering new materials with superior properties. However, the high design flexibility in materials, especially in the realm of metamaterials where the sub-wavelength structure provides an additional degree of freedom in design, poses a formidable computational cost in various real-world applications. With the advent of big data, deep learning brings revolutionary breakthroughs in many conventional machine learning and pattern recognition tasks such as image classification. The accompanied data-driven modeling paradigm also provides transformative methodology shift in materials science, from trial-and-error routine to intelligent material discovery and analysis. This review systematically summarize the application of deep learning in material science, based on a model selection perspective for both natural materials and metamaterials. The review aims to uncover the logic behind data-model relation with emphasis on suitable data structures for different scenarios in the material study and the corresponding problem-solving deep learning model architectures.

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“…Study [58] proposed developing prefabricated ceramics utilizing Machine Learning (ML). The model was trained by predetermined element analysis data combined with a self-learning algorithm to explore high-performance prefabricated ceramics in thermomechanical conditions.…”

Section: Commonly Used Ai-based Materials Science Applicationsmentioning

confidence: 99%

A Survey of Datasets, Preprocessing, Modeling Mechanisms, and Simulation Tools Based on AI for Material Analysis and Discovery

et al. 2022

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Research has become increasingly more interdisciplinary over the past few years. Artificial intelligence and its sub-fields have proven valuable for interdisciplinary research applications, especially physical sciences. Recently, machine learning-based mechanisms have been adapted for material science applications, meeting traditional experiments’ challenges in a time and cost-efficient manner. The scientific community focuses on harnessing varying mechanisms to process big data sets extracted from material databases to derive hidden knowledge that can successfully be employed in technical frameworks of material screening, selection, and recommendation. However, a plethora of underlying aspects of the existing material discovery methods needs to be critically assessed to have a precise and collective analysis that can serve as a baseline for various forthcoming material discovery problems. This study presents a comprehensive survey of state-of-the-art benchmark data sets, detailed pre-processing and analysis, appropriate learning model mechanisms, and simulation techniques for material discovery. We believe that such an in-depth analysis of the mentioned aspects provides promising directions to the young interdisciplinary researchers from computing and material science fields. This study will help devise useful modeling in the materials discovery to positively contribute to the material industry, reducing the manual effort involved in the traditional material discovery. Moreover, we also present a detailed analysis of experimental and computation-based artificial intelligence mechanisms suggested by the existing literature.

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Accelerated design of architectured ceramics with tunable thermal resistance via a hybrid machine learning and finite element approach

Cited by 30 publications

References 47 publications

Architectured Ceramics: Interlocking Design, Programmable Laser Manufacturing and Testing

Architectured Ceramics: Interlocking Design, Programmable Laser Manufacturing and Testing

Deep learning modeling strategy for material science: from natural materials to metamaterials

A Survey of Datasets, Preprocessing, Modeling Mechanisms, and Simulation Tools Based on AI for Material Analysis and Discovery

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