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Meeting 2026 TMS Annual Meeting & Exhibition
Symposium Additive Manufacturing Modeling, Simulation and Artificial Intelligence
Presentation Title Machine Learning Based Adaptive Deposition Control for Wire Arc Additive Manufacturing Repair
Author(s) Anas Ullah Khan, Ajay V, Amber Shrivastava
On-Site Speaker (Planned) Anas Ullah Khan
Abstract Scope Wire arc additive manufacturing (WAAM) has strong potential to repair damaged or fractured metal components owing to high deposition rates and minimal material waste. However, damage creates random irregular surfaces, and fixed travel speed deposition results in uniform bead height, conforming to the damaged surface and producing unpredictable outcomes. This work utilizes a single-layer LSTM (long short-term memory) model with 128 units to predict the optimal travel speed profile required to achieve a flat top surface before each deposition. The model, trained on bead-height and path history data, dynamically adjusts deposition speed to conform to complex geometries. Strength testing and numerical simulation were also performed to assess the structural and geometric effects of variable-height layers. LSTM-driven speed prediction reduced the standard deviation of top-layer height from 1.65 mm to 1.02 mm compared with a conventional fixed-speed deposition. These results validate the feasibility of travel-speed control in WAAM repair.
Proceedings Inclusion? Planned:
Keywords Additive Manufacturing, Machine Learning, Modeling and Simulation

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

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Columnar-To-Equiaxed Transition in Laser Fusion Additive Manufacturing
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Crystal Plasticity–Informed Surrogate Modeling for Predictive Laser Powder Bed Fusion Simulation of Ti-6Al-4V
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F-22: An Artificial Neural Network Modeling Approach to Electroforming of Micro-Tubes in the Presence of Surfactants
F-23: Computational Thermodynamics-Based Optimization of Ti-6Al-4V via Al and V Substitution for Additive Manufacturing
F-24: Convolution Tensor Decomposition Method for Efficient High-Resolution Solutions to the Allen-Cahn Equation: Application to Grain Growth Simulations in Additive Manufacturing
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F-27: Expanding Capabilities to Simulate Powder Bed Fusion Process With Additive Manufacturing Module
F-28: Experimental‑CFD-Based Observation of Thermo‑Fluid Mechanisms in LPBF with Nanoparticle‑Coated Powders
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F-36: Predictive Multi-Physics Multi-Material Design of Refractory Materials in Laser Powder Bed Fusion Additive Manufacturing
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Identifying LPBF Anomalies and Creep Damage in Nickel Based Super Alloys
Image-Based Uncertainty Quantification of Geometric Deviations in Additive Manufacturing
Integrated Melt Pool Dynamics and Defect Prediction in Additive Manufacturing of Inconel 625
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Leveraging Laser Parameters and Layer Remelting to Tailor Microstructure in Laser Powder Bed Fusion
LPBF Microstructure Modeling for Multiple Materials
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