基于深度学习和迁移学习的拓扑优化结构增材制造设计

Maede Mohseni, Saeed Khodaygan
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摘要

目的 本文旨在提高拓扑优化(TO)结构增材制造(AM)的可制造性。提高可制造性的重点是修改几何约束和对 AM 零件的构建方向 (BO) 进行分类,以减少应力和支撑结构 (SS)。为此,正在开发人工智能(AI)网络,以实现增材制造设计(DfAM)的自动化。本研究考虑了三种几何约束,通过卷积自动编码器(CAE)和迁移学习(TL)对其进行修正。此外,还使用生成对抗(GAN)和分类网络对 AM 零件的 BO 进行分类,以减少 SS。为了验证结果,还进行了有限元分析(FEA),以比较修改后部件与原始部件的应力。此外,在人工智能预测的 BO 中通过激光粉末床熔融(LB-PBF)生产了一个样品,以观察其 SSs。有限元分析表明,提高可制造性可使应力降低 50%。此外,通过训练 GAN 和预训练 ResNet-18,训练、验证和测试的准确率分别达到 80%、95% 和 96%。使用 LB-PBF 生产的样品表明,ResNet-18 预测的 BO 不需要 SS。因此,复杂的 TO 零件可以最可行的方式进行设计,并通过 AM 技术以最少的材料用量、残余应力和变形进行制造。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Design for additive manufacturing of topology-optimized structures based on deep learning and transfer learning
Purpose This paper aims to improve the manufacturability of additive manufacturing (AM) for topology-optimized (TO) structures. Enhancement of manufacturability focuses on modifying geometric constraints and classifying the building orientation (BO) of AM parts to reduce stresses and support structures (SSs). To this end, artificial intelligence (AI) networks are being developed to automate design for additive manufacturing (DfAM). Design/methodology/approach This study considers three geometric constraints for their correction by convolutional autoencoders (CAEs) and transfer learning (TL). Furthermore, BOs of AM parts are classified using generative adversarial (GAN) and classification networks to reduce the SS. To verify the results, finite element analysis (FEA) is performed to compare the stresses of modified components with the original ones. Moreover, one sample is produced by the laser-based powder bed fusion (LB-PBF) in the BO predicted by the AI to observe its SSs. Findings CAE and TL resulted in promoting the manufacturability of TO components. FEA demonstrated that enhancing manufacturability leads to a 50% reduction in stresses. Additionally, training GAN and pre-training the ResNet-18 resulted in 80%, 95% and 96% accuracy for training, validation and testing. The production of a sample with LB-PBF demonstrated that the predicted BO by ResNet-18 does not require SSs. Originality/value This paper provides an automatic platform for DfAM of TO parts. Consequently, complex TO parts can be designed most feasibly and manufactured by AM technologies with minimal material usage, residual stresses and distortions.
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