Record link: 
http://hdl.handle.net/20.500.12708/226515
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Title: 
Approach for weld bead geometry prediction to optimize process planning in wire arc additive manufacturing applications
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Citation: 
Hoffmann, M., Maier, S., Mayr, C., Mauthner, G., Trautner, T. F., & Bleicher, F. (2024). Approach for weld bead geometry prediction to optimize process planning in wire arc additive manufacturing applications. In 18th CIRP Conference on Intelligent Computation in Manufacturing Engineering (pp. 668–673). https://doi.org/10.1016/j.procir.2026.01.115
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Publisher DOI: 
10.1016/j.procir.2026.01.115
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Publication Type: 
Inproceedings - Full-Paper Contribution
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Language: 
English
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Authors: 
Hoffmann, Maximilian 
Maier, Severin 
Mayr, Christian 
Mauthner, Gernot 
Trautner, Thomas Fabian 
Bleicher, Friedrich 
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Organisational Unit: 
E311-01-3 - Forschungsgruppe Steuerungstechnik und integrierte Systeme 
E311-01-2 - Forschungsgruppe Werkzeugmaschinen 
E311-01-1 - Forschungsgruppe Fertigungstechnologie 
E311-01-4 - Forschungsgruppe Fertigungsmesstechnik und adaptronische Systeme 
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Published in: 
18th CIRP Conference on Intelligent Computation in Manufacturing Engineering 
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Volume: 
138
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Date (published): 
2024
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Event name: 
16th CIRP Conference on Intelligent Computation in Manufacturing Engineering 
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Event date: 
10-Jul-2024 - 12-Jul-2024
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Event place: 
Neapel, Italy
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Number of Pages: 
6
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Keywords: 
Metal Additive Manufacturing; Closed-Loop Manufacturing; Data Management; Artificial Neural Network; CAD/CAM
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Abstract: 
To enable the industrial use of wire arc additive manufacturing (WAAM), it is crucial to provide effective work preparation through process planning systems such as computer-aided-manufacturing (CAM) software. However, current CAM software systems are limited regarding deposition, path manipulation and simulation. To overcome these issues, this paper presents an artificial neural network (ANN) driven methodology for the prediction and integration of effective weld bead geometry information. The approach comprises three phases encompassing the steps of automated data processing, data-based modelling of weld bead geometry and the utilization of this additional information through CAM integration.
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Project title: 
Advanced Processing of Additively Manufactured Parts II: 40089127 (FFG - Österr. Forschungsförderungs- gesellschaft mbH) 
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Research Areas: 
Digital Transformation in Manufacturing: 50%
Sustainable Production and Technologies: 10%
Automation and Robotics: 40%
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Science Branch: 
2030 - Maschinenbau: 70%
2050 - Werkstofftechnik: 15%
2020 - Elektrotechnik, Elektronik, Informationstechnik: 15%
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Appears in Collections:
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