Session: DAC-04-2: Data-Driven Design

Paper Number: 132122

132122 - Automatic Knowledge Extraction for Decision Support in the Structural Design Process 

Much of an engineer's implicit knowledge and experience is embedded in data from previous engineering processes. In the typical automotive structural design process, many simulation variants are created to incrementally improve relevant performance values. Engineers decide about the modifications to make to the geometry to achieve these improvements. The steps of this development process reflect the engineer's decisions and thus her/his knowledge and experience. This implicit knowledge is potentially helpful for new projects, but is not easily accessible. This paper proposes a method to automatically extract past solutions from a database containing finite element simulation data from previous development processes. A reference model with high similarity to the problem at hand is identified in the database. Since the development process of this model is already completed, there must exist a later version of the model where the problem has been solved. The difference between the reference model and the identified improved model represents an engineer's knowledge that was used to solve a similar problem in the past. Possible solutions identified this way are successfully used to support engineers in their decisions in new projects during the structural design process. The method is applied to pedestrian safety data from the structural design process of cars using real-world data.

Presenting Author: Sonja Schlenz BMW Group

Presenting Author Biography: Sonja Schlenz received her Master's degree in mechanical engineering from FAU Erlangen, Germany, in 2020. Since 2021, she has been a PhD student at BMW Group in cooperation with the Technical University of Munich. Her research interests include geometry representation, machine learning for knowledge transfer and the support of engineering design with AI methods.

Authors:

Sonja Schlenz BMW Group
Simon Mößner BMW Group
Carl Henrik Ek University of Cambridge
Fabian Duddeck Technical University of Munich