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EFFICIENT FORMALISATION OF TECHNICAL REQUIREMENTS FOR GENERATIVE ENGINEERING

Published online by Cambridge University Press:  19 June 2023

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, [email protected]

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.

Type
Article
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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