Challenges and Opportunities of Software-Based Production Planning and Control for Engineer-to-Order Manufacturing

Bründl, Patrick; Stoidner, Micha; Nguyen, Huong Giang; Baechler, Andreas; Franke, Jörg

doi:10.1007/978-3-031-43670-3_5

IFIP Advances in Information and Communication Technology

2023

DOI: 10.1007/978-3-031-43670-3_5

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Challenges and Opportunities of Software-Based Production Planning and Control for Engineer-to-Order Manufacturing

Patrick Bründl,

Micha Stoidner,

Huong Giang Nguyen

et al.

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Cited by 11 publications

References 47 publications

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

Bründl,

Scheffler,

Stoidner

et al. 2023

J Intell Manuf

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

Bründl,

Scheffler,

Stoidner

et al. 2023

J Intell Manuf

View full text Add to dashboard Cite

show abstract

Optimization of Process, Knowledge, and Manufacturing Management in Customized Production: A Graph-Based Approach for Manufacturing Planning

Bründl,

Stoidner,

Nguyen

et al. 2024

IFAC-PapersOnLine

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Towards a Time Data Library to Enable Process Optimization in Engineer-to-Order Industries: The Case of Control and Switchgear Manufacturing

Stoidner,

Bründl,

Nguyen

et al. 2024

IFAC-PapersOnLine

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Challenges and Opportunities of Software-Based Production Planning and Control for Engineer-to-Order Manufacturing

Cited by 11 publications

References 47 publications

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

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

Optimization of Process, Knowledge, and Manufacturing Management in Customized Production: A Graph-Based Approach for Manufacturing Planning

Towards a Time Data Library to Enable Process Optimization in Engineer-to-Order Industries: The Case of Control and Switchgear Manufacturing

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