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

SP-7：基于多尺度方法的植入物计算机设计

• 批准号: 495863685
• 负责人: Professor Dr.-Ing. Philipp Junker
• 金额: --
• 依托单位: Institut für Kontinuumsmechanik (IKM)
• 依托单位国家: 德国
• 项目类别: Research Units
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/495863685?language=en
• 关键词: SP; silico; design; implants; based

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.

研究小组将开发优化的永久植入物。增材制造在几何设计方面带来了很大的自由度。因此，可以有针对性地调整植入物中的晶格结构，以使植入物最佳地适应周围的骨骼。资助期 1 重点关注永久性植入物。特别是，必须在长时间的负载下保证植入物的功能。在该 SP-7 中，开发了一个跨尺度模型，该模型考虑了微观尺度上损伤效应的影响、中尺度上网格结构的缺口效应以及宏观尺度上的应力屏蔽的影响。为此，正在引入一种新型的同质化方法，该方法允许使用机器学习以省时的方式链接秤。此外，还进一步开发了热力学拓扑优化，以确定所有尺度的最佳数字植入物，同时考虑到与过程相关的损坏和应力引起的疲劳效应。为了找到晶格结构和功能之间的最佳值，开发了一种有效的多尺度算法。在高应力（HCF，高循环疲劳）和非常高的负载循环次数（VHCF，极高循环疲劳）下的疲劳行为是在微观尺度上建模的。假设失效主要发生在晶界处。晶格结构对应力-应变关系的影响的研究是在介观尺度上进行的。植入物在疲劳强度、承载能力和形态方面的优化最终是在宏观尺度上进行的。各个尺度之间的数据传输将基于专门开发的人工神经网络来实现。