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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. [ABSTRACT FROM AUTHOR]

The reuse of existing design models offers great potential in saving resources and generating an efficient workflow. In order to fully benefit from these advantages, it is necessary to develop methods that are able to retrieve mechanical engineering geometry from a query input. This paper aims to address this problem by presenting a method that focuses on the needs of product development to retrieve similar components by comparing the geometrical similarity of existing parts. Therefore, a method is described, which first converts surface meshes into point clouds, rotates them, and then transforms the results into matrices. These are subsequently passed to a pre-trained Deep Learning network to extract the feature vector. A similarity between different geometries is calculated and evaluated based on this vector. The procedure employs a new type of part alignment, especially developed for mechanical engineering geometries. The method is presented in detail and several parameters affecting the accuracy of the retrieval are discussed. This is followed by a critical comparison with other shape retrieval approaches through a mechanical engineering benchmark data set. [Display omitted] • Method for retrieval of mechanical engineering components. • Transformation of geometries with the projection method into matrices. • Application of pre-trained Deep Learning networks to generate the feature vector. • Improving retrieval results through new alignment method. • Comparison of the new procedure with state of the art methods. [ABSTRACT FROM AUTHOR]

Geometric deviations are inevitably observable on every manufactured workpiece. These deviations affect the function and quality of mechanical products and have therefore to be controlled by geometric tolerances. Computer-aided tolerancing aims at supporting design, manufacturing, and inspection by determining and quantifying these effects of geometric deviations on the product quality and the functional behaviour. However, most established tolerance representation schemes imply abstractions of geometric deviations and are not conform with the standards for geometric dimensioning and tolerancing. These limitations led to the development of a Skin Model inspired framework for the tolerance analysis, which is based on a representation of non-ideal workpieces employing discrete geometry representation schemes, such as point clouds and surface meshes. In this contribution, this Skin Model inspired framework for computer aided tolerancing is extended to systems in motion and applied to the tolerance analysis of rotating mechanism with higher kinematic pairs. For this purpose, the generation of non-ideal part representatives, as well as their processing with algorithms for registration and computational geometry are highlighted. Finally, the results are visualized and interpreted. The procedure as well as the simulation model itself are shown in a case study of a disk cam mechanism. [ABSTRACT FROM AUTHOR]