Physics-Inspired Neural Networks in the Evaluation, Generation and Design of Frame Structures

物理启发的神经网络在框架结构的评估、生成和设计中的应用

• 批准号: 523871886
• 负责人: Professor Dr.-Ing. Sandro Wartzack
• 金额: --
• 依托单位: Lehrstuhl für Konstruktionstechnik (KTmfk)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/523871886?language=en
• 关键词: Physics; Inspired; Neural; Networks; Evaluation

Structural optimization represents an economical and effective lightweight design method, especially when full material utilization in terms of strength and stiffness is desired. The design and evaluation of truss structures is one of the most common tasks in practice, often by using numerical simulation with beam or truss elements. In this work, alternative design and evaluation procedures of such 1D idealizations based on so-called physical-inspired neural networks (PINN) are the focus of research. Thereby, mainly 3D simulation data and 3D topology optimization results shall serve as a training basis to improve the predictive behaviour of the 1D idealizations. In total, three different PINNs will be investigated. The first PINN is expected to lead to improved prediction of physical quantities such as deformation and strain of 1D models. The second PINN is intended to derive optimal cross-section parameters based on a given 1D frame structure. The third PINN will use training data from 3D optimizations to predict optimal design proposals for frame structures so that, for example, regions with multi-axial states can be directly optimized and derived as a parametric model without the need for complex topology optimization. In addition to the training of PINNs, a method based on the so-called skeletonization for the fully automatic transfer of results from a 3D simulation to a 1D model is also investigated. This fully automatic transfer is necessary to generate the synthetic data sets for the respective PINNs. Finally, the trained PINNs are combined to realize an automated evaluation, cross-section dimensioning and locally optimized regions in real time (a few seconds) for a bicycle frame, for example.

结构优化代表了一种经济有效的轻量化设计方法，特别是当需要充分利用材料的强度和刚度时。桁架结构的设计和评估是实践中最常见的任务之一，通常使用梁或桁架单元的数值模拟。在这项工作中，基于所谓的物理启发神经网络（PINN）的这种一维理想化的替代设计和评估程序是研究的重点。因此，主要以 3D 仿真数据和 3D 拓扑优化结果作为训练基础，以改进 1D 理想化的预测行为。总共将研究三种不同的 PINN。第一个 PINN 预计将改进对一维模型的变形和应变等物理量的预测。第二个 PINN 旨在根据给定的一维框架结构导出最佳横截面参数。第三个 PINN 将使用 3D 优化的训练数据来预测框架结构的最佳设计方案，例如，具有多轴状态的区域可以直接优化并导出为参数模型，而不需要复杂的拓扑优化。除了 PINN 的训练之外，还研究了一种基于所谓的骨架化的方法，用于将结果从 3D 模拟全自动转移到 1D 模型。这种全自动传输对于生成各个 PINN 的合成数据集是必要的。最后，将经过训练的 PINN 组合起来，以实现自行车车架等的实时（几秒）自动评估、横截面尺寸标注和局部优化区域。