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Gas Metal Arc Welding (GMAW) is a critical industrial technique known for its high productivity, flexibility, and adaptability to automation. Despite the significant advancements in robotic welding, challenges remain in fully automating the arc welding process, particularly due to the complex dynamics of the weld pool associated with GMAW. A human-robot collaborative (HRC) system where humans operate robots may conveniently provide the needed adaptive control to the complex GMAW. While in conventional HRC systems humans receive process feedback to make adaptive adjustments, we propose provide humans with predictive future feedback to further ease the human decision and reduce the needed skills/trainings. To this end, this study explores the integration of deep learning models, specifically Generative Adversarial Networks (GANs) combined with Gated Recurrent Units (GRUs), to model and predict the dynamic behavior of the weld pool during GMAW. By leveraging time-series data of torch movement and corresponding weld pool images, the proposed GRU-GAN model generates high-fidelity weld pool images, capturing the intricate relationship between speed variations and weld pool morphology. Through extensive experimentation, including the design of an acceptable Encoder-Decoder structure for the GAN, we demonstrate that incorporating both temporal and speed sequence information significantly enhances the model's predictive capabilities. The findings validate the hypothesis that dynamic torch speed adjustments, akin to those performed by skilled human welders, can be effectively modeled to improve the quality of automated welding processes. Future work will be devoted to human-based model predictive control (MPC) in an HRC environment.
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USING NON-GRAVITY ALIGNED WELDING IN LARGE SCALE ADDITIVE METALS MANUFACTURING FOR BUILDING COMPLEX PARTS
One of the most difficult aspects of printing large, complex metal parts is building large overhangs without the use of support structures. When using typical gas metal arc welding techniques, the torch is kept aligned with the gravitational direction. It has been shown that the maximum overhang angle that can be achieved is roughly 25°. This maximum can be increased by using part positioner, but this adds extra system complexity, especially for creating the robot paths. It is desirable then to develop a method of printing with the torch in a Non-Gravity Aligned (NGA) direction, such that the weld pool is supported and will produce the desired weld bead. This work focuses on the development of a control scheme based on sensor feedback of the state of the weld pool to maintain a stable, desired weld pool shape and thus print more complex parts using the gas metal arc welding process.
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- Award ID(s):
- 1822186
- PAR ID:
- 10159186
- Date Published:
- Journal Name:
- Solid Freeform Fabrication 2019: Proceedings of the 30th Annual International Solid Freeform Fabrication Symposium – An Additive Manufacturing Conference
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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