Efficient Formalisation of Technical Requirements for Generative Engineering
Year: 2023
Editor: Kevin Otto, Boris Eisenbart, Claudia Eckert, Benoit Eynard, Dieter Krause, Josef Oehmen, Nad
Author: Gräßler, Iris (1); Preuß, Daniel (1); Brandt, Lukas (2); Mohr, Michael (3)
Series: ICED
Institution: 1: Heinx Nixdorf Institute / Paderborn University;2: Atos Information Technology GmbH;3: EDAG Engineering GmbH
Section: Design Methods
Page(s): 1595-1604
DOI number: https://doi.org/10.1017/pds.2023.160
ISBN: -
ISSN: -
Abstract
Currently, engineers need to manually analyse requirement specifications for determining parameters to create geometries in generative engineering. This analysis is time-consuming, error-prone and causes high costs. Generative engineering tools (e.g. Synera) cannot interpret natural language requirements directly. The requirements need to be formalised in a machine-readable format. AI algorithms have the potential to automatically transform natural language requirements into such a formal, machine-readable representation. In this work, a method for formalising requirements for generative engineering is developed and implemented as a prototype in Python. The method is validated in a case example using three products of an automotive engineering service provider. Requirements to be formalised are identified in the specifications of these three products, which are used as a test set to evaluate the performance of the method. The results show that requirements for generative engineering are formalised with high performance (F1 of 86.55 %). By applying the method, efforts and therefore costs for manually analysing requirements regarding parameters for generative engineering are reduced.
Keywords: Requirements, Artificial intelligence, Semantic data processing, Computational design methods