G-code Net: Learning-based rational design and optimization for additively manufactured structures

IF 1.8 4区材料科学 Q3 MATERIALS SCIENCE, MULTIDISCIPLINARY

MRS Communications Pub Date : 2024-03-05 DOI:10.1557/s43579-024-00532-9

引用次数: 0

Abstract

In this work, we propose an integrated approach for the rational design of 3D-printed parts using machine learning. A Long Short-Term Memory (LSTM) neural network (G-code Net) was used to construct the functional relationship between the G-code and the mechanical properties of the 3D-printed part. Results show that a well-trained G-code Net can make accurate predictions of the mechanical responses and achieve a speed-up of three orders of magnitude compared to finite element. By further combining with genetic algorithm, one can efficiently perform inverse designs for 3D-printed parts with target mechanical response under other design constraints.

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G-code Net：基于学习的快速成型结构合理设计与优化

摘要在这项工作中，我们提出了一种利用机器学习合理设计三维打印部件的综合方法。我们使用长短期记忆（LSTM）神经网络（G-code Net）来构建 G 代码与三维打印部件机械性能之间的函数关系。结果表明，训练有素的 G-code Net 可以准确预测机械响应，与有限元相比，速度提高了三个数量级。通过与遗传算法进一步结合，可以在其他设计约束条件下高效地对具有目标机械响应的三维打印部件进行反向设计。图形摘要

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

MRS Communications MATERIALS SCIENCE, MULTIDISCIPLINARY-

CiteScore

2.60

自引率

10.50%

发文量

166

审稿时长

>12 weeks

期刊介绍： MRS Communications is a full-color, high-impact journal focused on rapid publication of completed research with broad appeal to the materials community. MRS Communications offers a rapid but rigorous peer-review process and time to publication. Leveraging its access to the far-reaching technical expertise of MRS members and leading materials researchers from around the world, the journal boasts an experienced and highly respected board of principal editors and reviewers.