Approach for weld bead geometry prediction to optimize process planning in wire arc additive manufacturing applications

Hoffmann, M.; Maier, S.; Mayr, C.; Mauthner, G.; Trautner, T.; Bleicher, F.

doi:10.1016/j.procir.2026.01.115

Record link:

http://hdl.handle.net/20.500.12708/226515

Title:

Approach for weld bead geometry prediction to optimize process planning in wire arc additive manufacturing applications

Citation:

Hoffmann, M., Maier, S., Mayr, C., Mauthner, G., Trautner, T., & Bleicher, F. (2024). Approach for weld bead geometry prediction to optimize process planning in wire arc additive manufacturing applications. In R. Teti (Ed.), 18th CIRP Conference on Intelligent Computation in Manufacturing Engineering (pp. 668–673). Elsevier. https://doi.org/10.1016/j.procir.2026.01.115

CatalogPlus:

AC18092333

Publisher DOI:

10.1016/j.procir.2026.01.115

Publication Type:

Inproceedings - Full-Paper Contribution

Language:

English

Authors:

Hoffmann, M.
Maier, S.
Mayr, C.
Mauthner, G.
Trautner, T.
Bleicher, F.

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

Published in:

18th CIRP Conference on Intelligent Computation in Manufacturing Engineering

Volume:

138

Date (published):

2024

Event name:

18th CIRP Conference on Intelligent Computation in Manufacturing Engineering (CIRP ICME ‘24)

Event date:

10-Jul-2024 - 12-Jul-2024

Event place:

Ischia, Italy

Number of Pages:

Publisher:

Elsevier

Keywords:

Metal Additive Manufacturing; Closed-Loop Manufacturing; Data Management; Artificial Neural Network; CAD/CAM

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.

Project title:

Advanced Processing of Additively Manufactured Parts II: 40089127 (FFG - Österr. Forschungsförderungs- gesellschaft mbH)

Research Areas:

Digital Transformation in Manufacturing: 50%
Sustainable Production and Technologies: 10%
Automation and Robotics: 40%

Science Branch:

2030 - Maschinenbau: 70%
2050 - Werkstofftechnik: 15%
2020 - Elektrotechnik, Elektronik, Informationstechnik: 15%

License:

CC BY-NC-ND 4.0