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EFFICIENT FORMALISATION OF TECHNICAL REQUIREMENTS FOR GENERATIVE ENGINEERING

Published online by Cambridge University Press:  19 June 2023

Iris Gräßler ,
Daniel Preuß ,
Lukas Brandt  and
Michael Mohr
Iris Gräßler
Affiliation:
Heinx Nixdorf Institute / Paderborn University
Daniel Preuß*
Affiliation:
Heinx Nixdorf Institute / Paderborn University
Lukas Brandt
Affiliation:
Atos Information Technology GmbH
Michael Mohr
Affiliation:
EDAG Engineering GmbH
*
Preuß, Daniel, Heinx Nixdorf Institute / Paderborn University, Germany, daniel.preuss@hni.upb.de

Abstract

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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

RequirementsArtificial intelligenceSemantic data processingComputational design methods
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 1595 - 1604
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

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