Artificial Intelligence and Large Language Models in CAD
Publicerad
Författare
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
The process of Finite Element Analysis (FEA) of components necessitates a CAD model that has
been appropriately tagged and prepared. This tagging involves identifying and selecting specific
parts of the CAD geometry for the application of boundary conditions, forces, or mesh specifications.
While manual tagging is feasible for simple geometries, it becomes extremely complex and
time-consuming for the intricate and large-scale components used by GKN Aerospace. To address
this challenge, GKN Aerospace developed an in-house automated tool to streamline certain aspects
of the tagging process, thereby saving time and effort. However, the tool requires proficiency in
YAML, a programming language unfamiliar to many analysis engineers, complicating its usage
and highlighting the need for a more user-friendly interface.
The aim of this project was to explore the capabilities of Artificial Intelligence and Large Language
Models in assisting with tasks such as comprehending and translating natural language terminology
of geometric features and using the capabilities of these generative models to create extraction
queries in SQL formatted in YAML. It is hypothesized that the capabilities of generative models,
text in this case, could be useful in making the geometry tagging process used by the automated tool
much more streamlined and simple to use, as it only requires natural language input and not complex
code that may be subject to formatting errors. While there are multiple ways to carry out the tagging
process, this project focuses only on the process used by the "Autotag" automated tagging tool.
A set of research questions were formulated to guide the project and ensure it was focused on
the right outcomes. The project was conducted over a period of 4 months. during the initial
phases of the project, interviews were conducted to better understand the challenges and issues
currently being faced by the engineers and GKN. Literature reviews were also conducted to gather
information about the advantages and disadvantages about LLMs and AI and ways in which these
technologies could be used to solve the issues that were identified.
An application was then developed that addressed these problems, and was integrated into the
"Autotag" tool in Sim-Center. The development, carried out with knowledge from literature reviews
and GKN engineer interviews utilizes Large Language Models (LLMs) to simplify feature selection
and identification. It allows use of natural language to ease communication between the user
and the application. While limitations exist due to LLMs and complex CAD models, the project
showcased a possible way in which LLMs can be used to augment the tagging process where manual
selection may not be possible.
Beskrivning
Ämne/nyckelord
Artificial Intelligence, Large language models, CAD, Finite Element Analysis, Engineering Design