Team of the chair
Dr.- Ing. Anahita Pakiman
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Biography
Knowledge Graph for CAE-based Development in Vehicle Safety
This dissertation presents a comprehensive exploration of Knowledge
Graphs (KGs) to transform the domain of vehicle development, with a
primary focus on improving crashworthiness. The proposed KG leverages
insights from diverse structured and unstructured data sources,
encompassing critical concepts within the automotive domain. This research
serves the industrial goal of capturing and preserving knowledge
derived from Computer-Aided Engineering (CAE) workflows, enabling
data reuse, improving guidelines, and facilitating Machine Learning
(ML) applications.
Creating a domain-specific KG is challenging, especially when dealing
with complex CAE data with intricate 3D deformations. This complexity
is rarely encountered in most existing text-based KGs. This thesis
presents a comprehensive path from data modelling to practical ML applications
based on a concrete use case to address this challenge. This
approach provides a cyclical feedback loop between data modelling and
feature extraction, enhancing the utility of both processes.
The research encompasses several key aspects, including data modelling
and ontology development, feature engineering, data visualisation for
knowledge discovery, and ML implementations to predict relationships
between simulations. It innovatively transforms crashworthiness analysis
into graph representations, uses SimRank to analyse weighted bipartite
graphs, and abstracts vehicle structures to rank simulations based
on load-path similarity.
Additionally, a user-friendly web application for this project is developed
using Django and hosted on GitHub1, ensuring accessibility and
ease of use for industry professionals and researchers alike. This work
highlights the immense potential of KGs in the automotive sector, facilitating
a data-driven approach to vehicle development and significantly
improving crashworthiness analysis through the integration of
CAE data.
