In this work, we establish a method for abstracting information from Computer Aided Engineering (CAE) into graphs. Such graph representations of CAE data can improve design guidelines and support recommendation systems by enabling the comparison of simulations, highlighting unexplored experimental designs, and correlating different designs. We focus on the load-path in crashworthiness analysis, a complex sub-discipline in vehicle design. The load-path is the sequence of parts that absorb most of the energy caused by the impact. To detect the load-path, we generate a directed weighted graph from the CAE data. The vertices represent the vehicle's parts, and the edges are an abstraction of the connectivity of the parts. The edge direction follows the temporal occurrence of the collision, where the edge weights reflect aspects of the energy absorption. We introduce and assess three methods for graph extraction and an additional method for further updating each graph with the sequences of absorption. Based on longest-path calculations, we introduce an automated detection of the load-path, which we analyse for the different graph extraction methods and weights. Finally, we show how our method for the detection of load-paths helps in the classification and labelling of CAE simulations.

翻译：本文建立了一种将计算机辅助工程（CAE）信息抽象为图的方法。CAE数据的图表示能够通过实现仿真间的对比、突出未探索的实验设计以及关联不同设计，从而改进设计指南并支持推荐系统。我们聚焦于车辆设计中复杂的子学科——耐撞性分析中的载荷路径。载荷路径是指吸收碰撞所产生大部分能量的零件序列。为检测载荷路径，我们根据CAE数据生成有向加权图：顶点代表车辆零件，边为零件连接性的抽象表达；边的方向遵循碰撞的时间顺序，边的权重反映能量吸收的某些方面。我们提出并评估了三种图提取方法，以及一种对每个图进行能量吸收序列更新的附加方法。基于最长路径计算，我们引入了载荷路径的自动检测方法，并针对不同图提取方法与权重对该检测进行分析。最后，我们展示了该载荷路径检测方法如何辅助CAE仿真的分类与标注。