Abstract
Wire Arc Additive Manufacturing (WAAM) offers significant potential for producing large, complex components with short lead times. While progress in WAAM technology has primarily been made in the field of gas metal arc welding (GMAW), plasma arc welding (PAW) has not yet been sufficiently researched. PAW offers distinct advantages due to its ability to easily separate heat input from material deposition, thereby providing greater flexibility in parameter optimization than GMAW. Meeting stringent geometric specifications is crucial to the success of any WAAM process. This entails precise determination of initial process parameters, and online parameter adjustments while stabilizing the build process. To address this challenge, we propose a hybrid modeling approach that integrates physics-based principles and data-driven insights. This approach yields static models, validated by experimental analysis, that facilitates the prediction of energy and material inputs to achieve desired bead geometries. Additionally, a model-based height error estimator is introduced that does not rely on any vision-based measurement systems, further enhancing applicability and economic viability. The developed models are integrated into a two-degree-of-freedom control strategy and validated by building representative workpieces. The validation demonstrates the potential of the proposed approach in predicting and controlling WAAM processes.
Originalsprache | Englisch |
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Seiten (von - bis) | 12-23 |
Seitenumfang | 12 |
Fachzeitschrift | Journal of Manufacturing Processes |
Volume | 126 |
DOIs | |
Publikationsstatus | Veröffentlicht - 2024 |
Research Field
- Complex Dynamical Systems