Deformation and failure mechanisms of metallic additively manufactured triply periodic minimum surface structures

在工业中有利地使用拓扑优化的轻质蜂窝结构通常需要巧妙和灵活的生产技术，例如增材制造（AM）。AM允许几何形状的优化，例如关于部件的减轻的重量。然而，它有时具有降低的机械稳定性和性能，这是由于在微观（单支柱）、介观（晶胞）和宏观（整个晶格结构）尺度上自然引入缺陷造成的。因此，有必要很好地了解在所有的三个长度尺度的变形和破坏机制，这样的复杂结构。该项目旨在了解金属AM三周期最小表面（TPMS）结构的变形和失效机制。我们的方法是基于先进的表征，数据驱动和基于模型的方法相结合。一方面，我们的目标是调查这些结构的几何特征和力学行为，使用实验数据（即前和原位X射线计算机断层扫描，XCT）。另一方面，这方面的知识将推广使用模拟模型来预测未经测试的TPMS结构的机械性能（刚度，承载能力）。先进的贝叶斯技术与几何缺陷和本构参数的随机场表示相结合，用于确定系统的制造缺陷的几何形状以及空间分布的模型参数，包括不确定性量化预测模型预测的准确性。原位XCT成像和实验数据分析方法的优化和进一步发展是直接使用从数字体积相关获得的完整变形场的改进的模型校准技术的应用的先决条件。有几种机制导致TMPS结构的变形和损坏，一些与原材料（微观结构）有关，一些与制造参数（微观和介观效应，例如制造缺陷）有关，还有一些与几何特征（标称几何形状，壁厚偏差）有关。在该项目中，将分析这些影响：我们将识别几何缺陷，随后分析材料行为（例如，（an-）isotropy）。为了获得现实和全面的见解，将研究两种加载情况：压缩（广泛应用于AM蜂窝结构）和扭转（新）。作为最终的成就，变形和失效机制的识别将允许指定最相关的设计和制造参数，有助于机械行为和预测的机械性能和它的不确定性，直接通过一个仿真模型直接纳入制造缺陷。

The advantageous use of topologically optimized, light-weight cellular structures in industry often requires cunning and flexible production technologies, such as additive manufacturing (AM). AM allows for an optimization of the geometry e.g. with respect to a reduced weight of the component. However, it sometimes possesses reduced mechanical stability and performance caused by naturally introduces defects at the microscopic (single strut), mesoscopic (unit cell) and macroscopic (whole lattice structure) scale. It is therefore imperative to well understand the deformation and failure mechanisms of such complex structures at all of the three length scales. The proposed project aims at understanding the deformation and failure mechanisms of metallic AM Triply Periodic Minimum Surface (TPMS) structures. Our approach is based on a combination of advanced characterization, data-driven, and model-based methodologies. On the one hand, we aim to investigate the geometric features and mechanical behavior of these structures using experimental data (namely ex- and in-situ X-ray Computed Tomography, XCT). On the other hand, this knowledge will be generalized using simulation models to predict the mechanical behavior (stiffness, load bearing capacity) of untested TPMS structures. Advanced Bayesian techniques combined with a random field representation of geometrical defects and constitutive parameters are used to determine both systematic manufacturing deficiencies in the geometry as well as the spatial distribution of model parameters including an uncertainty quantification to predict the accuracy of the model prediction. The optimization and further development of in-situ XCT imaging and experimental data analysis methods are a precondition for the application of improved model calibration techniques that directly use the complete deformation field obtained from the digital volume correlation. There are a few mechanisms contributing to the deformation and damage of TMPS structures, some linked to the raw material (microstructure), some to the manufacturing parameters (micro- and mesoscopic effects, e.g. manufacturing defects), and some to the geometrical features (nominal geometry, wall thickness deviation). In the project, those effects are analyzed: we will identify geometrical defects and subsequently analyze the material behavior (e.g. (an-)isotropy). To derive realistic and comprehensive insights, two loading scenarios will be studied: compression (widely applied for AM cellular structures) and torsion (novel). As an ultimate achievement, the identification the deformation and failure mechanisms will allow specifying the most relevant design and manufacturing parameter contributing to the mechanical behavior and predicting the mechanical performance and its uncertainty directly by a simulation model directly incorporating manufacturing defects.