Comparing CAD part models for geometrical similarity: A concept using machine learning algorithms

Abstract The efficient execution of process planning activities requires the knowledge from several distinct domains. However, in this context, a common issue is the existence of collective knowledge in the one domain with a lack of networked expertise with other domains. Motivated by this, the paper proposes an approach to support process planning by comparing the generated part design with older, validated products. The utilization of this earlier CAD part models has the potential to reduce development costs, shorten the production start-up time and improve the product quality. The new concept provides the manufacturing personnel with a method for comparing the newly designed part with a pool of validated models to identify the most similar one. Each previous CAD part model is linked with the necessary manufacturing information, so that the initial values for the manufacturing process are available without a time consuming testing phase. The method is divided in three main steps: the global similarity comparison, the segmentation of the part and the local similarity comparison. In the first step, the geometry is projected onto a sphere and then transformed to a matrix. Afterwards these matrices are compared and clustered into corresponding groups. In the following step, a Machine Learning algorithm segments the objects into specific, manufacturing relevant groups. In every cluster, the segmented geometries are again compared for similarity. The combination of the first and the second ranking results in a global similarity hierarchy for the newly designed part. In this paper, the entire procedure is shown with the example of sheet-bulk metal formed parts. This new manufacturing process particularly benefits from this method, as the amount of data is still limited and therefore little expert knowledge exists.