Identification and Analyzing of Causal Chains in CAE

The presentation by Niklas Stalanich, Thomas Hinterdorfer, Andreas Kuhn from ANDATA, and Raul Garcia Gomez and Dennis Böhmländer from Aud focused on "Identification and Analyzing of Causal Chains in CAE." It highlighted the integration of Finite Element Analysis (FEA), Simulation Data Management Systems (SDMS), and Artificial Intelligence (AI) to automate in-depth analysis of FEA results and assure simulation quality standards in the automotive industry. The concept involves a new approach called "Wirkkettenanalyse" or the analysis of the chain of effects. This method helps understand the logical sequence of interacting components in complex structures, like vehicles. It aims to identify the relevant chains of effects for a given sensor signal, thereby answering questions about causality and influence of various components on each other. Application examples include causality studies, design target evaluations, estimation of uncertainties, sensitivity analysis, and performance comparison. The approach provides a way to automate various in-depth analyses by modeling causal relations for physical and mechanical problems. The analysis method involves detection of parts and segments, calculation of the effect structure, analysis of design evaluation and causalities, and sensitivity analysis. The approach uses advanced tools and software for automated detection, graph interpretation of effect structures, and criteria-based search problems. The innovation brings several advantages, such as enriching FEM post-processing analysis, automating in-depth analysis of FEM results, retrieving new analytics from old data in SDMS, enabling innovative forms of data mining, and laying the foundation for virtual product approval.