SP-7: In-silico design of implants based on a multi-scale approach

研究小组将开发优化的永久性植入物。增材制造在几何设计方面具有很大的自由度。因此，可以有针对性地调节植入物中的网格结构，以便使植入物最佳地适应周围的骨。第一阶段的资金重点是永久性植入。特别是，必须保证植入物在长时间负载下的功能性。在这个SP-7中，开发了一个跨尺度模型，该模型考虑了损伤效应对微尺度的影响，网格结构的切口效应对中尺度的影响，以及应力屏蔽对宏观尺度的影响。为此，正在引入一种新型的均匀化方法，该方法允许使用机器学习以具有时间效率的方式将尺度联系起来。此外，考虑到过程相关损伤和应力诱导疲劳效应，进一步开发热力学拓扑优化，以确定所有尺度下的最佳数字植入物。为了找到最佳的晶格结构和功能之间，一个有效的多尺度算法的发展。在微观尺度上模拟了高应力（HCF，高周疲劳）和极高载荷循环数（VHCF，极高周疲劳）下的疲劳行为。据推测，破坏主要发生在晶界。晶格结构对应力-应变关系的影响的研究发生在细观尺度上。植入物在疲劳强度、承载能力和形态方面的优化最终在宏观尺度上进行。各个天平之间的数据传输将基于专门开发的人工神经网络来实现。

Optimized permanent implants are to be developed in the research group. Additive manufacturing results in a great degree of freedom in terms of geometric design. As a result, the lattice structure in the implant can be adjusted in a targeted manner in order to optimally adapt the implant to the surrounding bone. Funding period 1 focuses on permanent implants. In particular, the functionality of the implant must be guaranteed over a long period of loading. In this SP-7, a cross-scale model is developed that takes into account the influence of damage effects on the microscale, of notch effects of the grid structures on the mesoscale, and stress shielding on the macroscale. To this end, a new type of homogenization approach is being introduced that allows the scales to be linked in a time-efficient manner using machine learning. In addition, the thermodynamic topology optimization is further developed in order to determine the optimal digital implant across all scales, taking into account process-related damage and stress-induced fatigue effects. In order to find the optimum between lattice structure and functionality, an efficient multi-scale algorithm is developed. The fatigue behavior under stress at high (HCF, High Cycle Fatigue) and very high number of load cycles (VHCF, Very High Cycle Fatigue) is modeled on the microscale. It is assumed that the failure occurs mainly at the grain boundaries. The investigation of the influence of the lattice structure on the stress-strain relationship takes place on the mesoscale. The optimization of the implant in terms of fatigue strength, load-bearing capacity, and morphology is ultimately carried out on the macro scale. The data transfer between the individual scales will be realized based on specially developed artificial neural networks.