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Prediction of mechanical properties of 3d printed lattice structures through machine learning

Ma, Shuai, Tang, Qian, Liu, Ying and Feng, Qixiang 2022. Prediction of mechanical properties of 3d printed lattice structures through machine learning. Journal of Computing and Information Science in Engineering 22 (3) , 031008. 10.1115/1.4053077

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Abstract

Lattice structures (LS) manufactured by 3D printing are widely applied in many areas, such as aerospace and tissue engineering, due to their lightweight and adjustable mechanical properties. It is necessary to reduce costs by predicting the mechanical properties of LS at the design stage since 3D printing is exorbitant at present. However, predicting mechanical properties quickly and accurately poses a challenge. To address this problem, this study proposes a novel method that is applied to different LS and materials to predict their mechanical properties through machine learning. First, this study voxelized 3D models of the LS units and then calculated the entropy vector of each model as the geometric feature of the LS units. Next, the porosity, material density, elastic modulus, and unit length of the lattice unit are combined with entropy as the inputs of the machine learning model. The sample set includes 57 samples collected from previous studies. Support vector regression (SVR) was used in this study to predict the mechanical properties. The results indicate that the proposed method can predict the mechanical properties of LS effectively and is suitable for different LS and materials. The significance of this work is that it provides a method with great potential to promote the design process of lattice structures by predicting their mechanical properties quickly and effectively.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: American Society of Mechanical Engineers (ASME)
ISSN: 1530-9827
Date of First Compliant Deposit: 2 November 2021
Date of Acceptance: 2 November 2021
Last Modified: 16 Feb 2022 12:30
URI: https://orca.cardiff.ac.uk/id/eprint/145264

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