Semantic part segmentation of spatial features via geometric deep learning for automated control cabinet assembly

Patrick Bründl (), Benedikt Scheffler, Micha Stoidner, Huong Nguyen, Andreas Baechler, Ahmad Abrass and Jörg Franke
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Patrick Bründl: Friedrich-Alexander-Universität Erlangen-Nürnberg
Benedikt Scheffler: Friedrich-Alexander-Universität Erlangen-Nürnberg
Micha Stoidner: Friedrich-Alexander-Universität Erlangen-Nürnberg
Huong Nguyen: Friedrich-Alexander-Universität Erlangen-Nürnberg
Andreas Baechler: Rittal GmbH & Co. KG
Ahmad Abrass: Rittal GmbH & Co. KG
Jörg Franke: Friedrich-Alexander-Universität Erlangen-Nürnberg

Journal of Intelligent Manufacturing, 2024, vol. 35, issue 8, No 5, 3695 pages

Abstract: Abstract 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.

Keywords: Geometric deep learning; Control cabinet; Digital twin; Surface mesh processing; Semantic part segmentation (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10845-023-02267-1

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