Abstract
Today’s success of industrial companies is largely determined by engineering competence and the digitization of all corporate processes. The design process and know-how of engineers is strongly individual and a rule-based description of their approach can often not be done at all or only with high effort. Existing knowledge can therefore only be passed on to other engineers with difficulty, which in particular increases the effort required for familiarisation. A further problem is the lack of an overview of existing components within a company, which very often leads to multiple designs and unnecessary waste of time for the engineer.The aim of this approach is to extract the implicit knowledge from existing CAD models with the aid of machine learning methods and thus to make it formalizable. In addition, a suitable classification and similarity analysis should quickly point out existing components. For this purpose, an AI-based assistance system is to be created. Based on the existing database, the assistant first points out to the engineer already existing, but very similar components. For that, the component type currently in construction firstly is identified and then very similar components are searched within the detected scale that are finally suggested to the engineer. The engineer now only has to parameterize the proposed components according to his application. In a further step, the assistant should also be able to suggest useful next design steps, which it has learned on the basis of the CAD data already available and their design history. The implicit experience knowledge that is contained in the existing CAD models thus ensures a design suitable for production and the avoidance of errors in the design.
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