Learning and Adaptation in Wire Arc Additive Manufacturing Bead Geometry Control
This paper proposes a data-driven approach using recurrent neural networks and one-step-ahead predictive control for bead geometry control in Wire Arc Additive Manufacturing (WAAM). By updating the model online to account for changing thermal conditions, it significantly improves bead height and width consistency.
Article intelligence
Key points
- Uses recurrent neural network to learn input-output dynamics of WAAM
- One-step-ahead predictive control improves bead geometry consistency
- Online adaptation compensates for changing thermal conditions
- Experiments show significant improvements over constant input and static model baselines
Why it matters
This matters because uses recurrent neural network to learn input-output dynamics of WAAM.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.29144] Learning and Adaptation in Wire Arc Additive Manufacturing Bead Geometry Control
[Submitted on 27 May 2026]
Title:Learning and Adaptation in Wire Arc Additive Manufacturing Bead Geometry Control
View a PDF of the paper titled Learning and Adaptation in Wire Arc Additive Manufacturing Bead Geometry Control, by Chen-Lung Lu and 1 other authors
View PDF HTML (experimental)
Abstract:Robotics Wire Arc Additive Manufacturing (WAAM) is governed by complex and nonlinear process dynamics coupling thermal field to the build geometry. The process may be regarded as a multi-input/multi-output dynamical system with welding torch speed and wire feed rate as inputs and weld bead deposition height and width as outputs. In this paper, we use the input/output data to learn a data-driven model and use it for weld planning and control. We show that a simple recurrent neural network architecture and one-step-ahead predictive control can improve the process performance in terms of height and width consistency. To account for the changing thermal conditions during the printing process, we update the learning model using prediction error from the previous layer. This adaptation step further improves the prediction accuracy and controller performance. Experiments on a robotic WAAM testbed with integrated line-scanner feedback significant improvements in height and width consistency compared to constant input and static model baselines. The proposed learning and adaptation framework provides a practical pathway toward robust, data-driven regulation of additive manufacturing processes.
Subjects:
Robotics (cs.RO); Systems and Control (eess.SY)
Cite as: arXiv:2605.29144 [cs.RO]
(or arXiv:2605.29144v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.29144
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Chen-Lung Lu [view email] [v1] Wed, 27 May 2026 22:15:24 UTC (7,085 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled Learning and Adaptation in Wire Arc Additive Manufacturing Bead Geometry Control, by Chen-Lung Lu and 1 other authors
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.RO
new | recent | 2026-05
Change to browse by:
cs cs.SY eess eess.SY
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)