EFFICIENT FORMALISATION OF TECHNICAL REQUIREMENTS FOR GENERATIVE ENGINEERING

Abstract Currently, engineers need to manually analyse requirement specifications for determining parameters 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 be formalised a machine-readable format. AI algorithms have the potential automatically transform into such formal, representation. In this work, method formalising developed implemented as prototype Python. validated case example using three products of an automotive service provider. Requirements are identified these products, which used test set evaluate performance method. results show that with (F1 86.55 %). By applying method, efforts therefore costs analysing regarding reduced.