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Lookup NU author(s): Dr Xinwei LiORCiD
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© 2024 Elsevier B.V.For their optimal directional mechanical properties, anisotropic microlattices hold significant importance as advanced materials in a diverse range of applications. However, a bottleneck in their developments may be encountered when the limitations of current methods have been reached. The emergence of a new technological era calls for novelties with both the materials design and their methods. Herein, we introduce a novel concept comprising of machine learning and additive manufacturing for the design and fabrication of advanced customizable anisotropic microlattices. Design methodology consists of (i) pseudo-randomizations of initial polyhedron struts geometries, (ii) subsequent characterization of their loading responses through finite element modelling and (iii) accelerated investigations through convolutional neural networks (CNN) and optimization based on defined criteria. CNN has showed the capabilities of producing high validation accuracies with further input sets that surpass the performance of initial training data. From the pool of data, structures with a diverse range of mechanical properties, based on desired applications, can be obtained. A selected few, based on defined criteria, are experimentally studied through laser powder bed fusion using stainless steel for the build. Mechanical properties are found to be highly architecture strut geometry dependent and their structural-property relationships are also fully elucidated through both experiments and finite element modelling. Overall, through this work, we aim to present a new digital manufacturing concept based on using artificial intelligence to assist in the design and customizable of microlattices for additive manufacturing fabrication.
Author(s): Li X, Wang P, Zhao M, Su X, Tan YH, Ding J
Publication type: Article
Publication status: Published
Journal: Additive Manufacturing
Year: 2024
Volume: 89
Print publication date: 05/06/2024
Online publication date: 12/06/2024
Acceptance date: 06/06/2024
ISSN (print): 2214-8604
ISSN (electronic): 2214-7810
Publisher: Elsevier B.V.
URL: https://doi.org/10.1016/j.addma.2024.104248
DOI: 10.1016/j.addma.2024.104248
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