Predictive modeling of lattice structure design for 316L stainless steel using machine learning in the L-PBF process

IF 1.7 4区 工程技术 Q3 MATERIALS SCIENCE, MULTIDISCIPLINARY
Karim Asami, Sebastian Roth, Michel Krukenberg, Tim Röver, Dirk Herzog, Claus Emmelmann
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引用次数: 0

Abstract

Lattice structures in additive manufacturing of 316L stainless steel have gained increasing attention due to their well-suited mechanical properties and lightweight characteristics. Infill structures such as honeycomb, lattice, and gyroid have shown promise in achieving desirable mechanical properties for various applications. However, the design process of these structures is complex and time-consuming. In this study, we propose a machine learning-based approach to optimize the design of honeycomb, lattice, and gyroid infill structures in 316L stainless steel fabricated using laser powder bed fusion (L-PBF) technology under different loading conditions. A dataset of simulated lattice structures with varying geometries, wall thickness, distance, and angle using a computational model that simulates the mechanical behavior of infill structures under different loading conditions was generated. The dataset was then used to train a machine learning model to predict the mechanical properties of infill structures based on their design parameters. Using the trained machine learning model, we then performed a design exploration to identify the optimal infill structure geometry for a given set of mechanical requirements and loading conditions. Finally, we fabricated the optimized infill structures using L-PBF technology and conducted a series of mechanical tests to validate their performance under different loading conditions. Overall, our study demonstrates the potential of machine learning-based approaches for efficient and effective designing of honeycomb, lattice, and gyroid infill structures in 316L stainless steel fabricated using L-PBF technology under different loading conditions. Furthermore, this approach can be used for dynamic loading studies of infill structures.
基于机器学习的316L不锈钢点阵结构设计预测建模
晶格结构在316L不锈钢增材制造中由于其良好的机械性能和轻量化特性而受到越来越多的关注。填充结构,如蜂窝状、晶格状和陀螺状,在各种应用中显示出实现理想机械性能的希望。然而,这些结构的设计过程复杂且耗时。在这项研究中,我们提出了一种基于机器学习的方法来优化设计采用激光粉末床熔合(L-PBF)技术制造的316L不锈钢在不同载荷条件下的蜂窝、晶格和陀螺填充结构。利用模拟填充结构在不同荷载条件下力学行为的计算模型,生成了具有不同几何形状、壁厚、距离和角度的模拟晶格结构数据集。然后使用该数据集训练机器学习模型,根据填充结构的设计参数预测其力学性能。利用训练有素的机器学习模型,我们进行了设计探索,以确定给定机械要求和加载条件下的最佳填充结构几何形状。最后,采用L-PBF技术制备了优化后的填充结构,并进行了一系列力学试验,验证了其在不同载荷条件下的性能。总的来说,我们的研究证明了基于机器学习的方法在不同载荷条件下对使用L-PBF技术制造的316L不锈钢的蜂窝、晶格和陀螺填充结构进行高效设计的潜力。此外,该方法可用于填充结构的动荷载研究。
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
CiteScore
3.60
自引率
9.50%
发文量
125
审稿时长
>12 weeks
期刊介绍: The Journal of Laser Applications (JLA) is the scientific platform of the Laser Institute of America (LIA) and is published in cooperation with AIP Publishing. The high-quality articles cover a broad range from fundamental and applied research and development to industrial applications. Therefore, JLA is a reflection of the state-of-R&D in photonic production, sensing and measurement as well as Laser safety. The following international and well known first-class scientists serve as allocated Editors in 9 new categories: High Precision Materials Processing with Ultrafast Lasers Laser Additive Manufacturing High Power Materials Processing with High Brightness Lasers Emerging Applications of Laser Technologies in High-performance/Multi-function Materials and Structures Surface Modification Lasers in Nanomanufacturing / Nanophotonics & Thin Film Technology Spectroscopy / Imaging / Diagnostics / Measurements Laser Systems and Markets Medical Applications & Safety Thermal Transportation Nanomaterials and Nanoprocessing Laser applications in Microelectronics.
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