A Dataset of Electrical Components for Mesh Segmentation and Computational Geometry Research - Scheffler, Benedikt; Bründl, Patrick; Nguyen, Huong Giang; Stoidner, Micha; Franke, Jörg (Scientific Data, 2024)
Data quality is of crucial importance in the field of automated or digitally assisted assembly. This paper presents a comprehensive data set of triangle meshes representing electrical and electronic components obtained by scraping Computer Aided Design (CAD) models from the Internet. Consisting of a total of 234 triangle meshes with labelled vertices, this data set was specifically created for segmentation tasks. Its versatility for multimodal tasks is underscored by the presence of various labels, including vertex labels, categories, and subcategories. This paper presents the data set and provides a thorough statistical analysis, including measures of shape, size, distribution, and inter-rater reliability. In addition, the paper suggests several approaches for using the data set, considering its multimodal characteristics. The data set and related findings presented in this paper are intended to encourage further research and advancement in the field of manufacturing automation, specifically spatial assembly.<br
Keywords: Electrical and electronic engineering; Mechanical engineering; Scientific data

Electrical and Electronic Components Dataset - Scheffler, Benedikt; Bründl, Patrick (2023)
The "Electrical and Electronic Components Dataset" is a comprehensive collection of 3D mesh models representing various electrical and electronic components. The dataset is designed to serve multiple purposes, including but not limited to, machine learning, computer vision applications, and computational geometry research. It can be an invaluable resource for researchers and practitioners in the fields of electrical engineering, computer science, and data science. A unique aspect of this dataset is the inclusion of labels from three independent expert raters. These labels specifically focus on the semantic segmentation of different features within each 3D model. The involvement of multiple raters adds a layer of robustness and allows for the evaluation of inter-rater reliability, which is a key factor in the development and validation of semantic segmentation algorithms. The multi-rater labeled data is ideal for training and evaluating machine learning models geared towards semantic part segmentation. It opens the door for creating more accurate and reliable segmentation algorithms that can be employed in various applications such as quality control, automated assembly, and advanced robotics in the electrical and electronics domain.<br
Keywords: Semantic Part Segmentation; Triangle Meshes; Electrical and Electronic Components; Computational Geometry Research

Semantic part segmentation of spatial features via geometric deep learning for automated control cabinet assembly - Bründl, Patrick; Scheffler, Benedikt; Stoidner, Micha; Nguyen, Huong Giang; Baechler, Andreas; Abrass, Ahmad; Franke, Jörg (Journal of Intelligent Manufacturing, 2023)
Industries with batch size one manufacturing philosophies for highly customized products have been largely limited in manufacturing automation. The control cabinet industry is particularly affected by this problem during the mounting and wiring of components due to high variety, variance, and complexity of components as well as handling tasks. Rapid advances in the field of machine learning are opening new possibilities for automating previously manual processes. This paper proposes a concept for identifying geometric features of electrical components that starts from STEP files and transforms them into modular metrics relevant to build a digital twin and (automatic)manufacturing. The architecture is tested on a self-aggregated and processed dataset of control cabinet components and achieves an average dice score of 65.27% and an intersection over union of 51.41% across all segmentation classes. In addition to semantic part segmentation of the components, the cluster, volume and surface centroids, the normal vectors and the size of each feature are computed. The paper evaluates the suitability of cutting-edge techniques such as diffusion as well as established deep learning architectures. The result is a hybrid end-to-end inference pipeline suitable for general spatial assembly processes.
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Keywords: Control cabinet; Digital twin; Geometric deep learning; Semantic part segmentation; Surface mesh processing