In terms of graph machine learning, we formulate this as graph classification problem where each class corresponds to a component ID from a catalog and the models are trained to predict the next required component. Therefore, we propose to analyze a data set of past assemblies composed of components from the same component catalog, represented as connected, undirected graphs of components, in order to suggest the next needed component. Selecting the right components is a cumbersome task for design engineers as they have to pick from a large number of possibilities. In computer-aided design (CAD), software tools support design engineers during the modeling of assemblies, i.e., products that consist of multiple components.
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