Wire Arc Additive Manufacturing (WAAM)

Research focus

Wire-Arc Additive Manufacturing (WAAM) or Wire-Arc Directed Energy Deposition (WA-DED, or DED-Arc) is an AM technology that combines arc welding with robotic motion to manufacture large metallic components layer-by-layer at high deposition rates. Our research group develops the scientific and engineering foundations of WAAM for structural-scale applications, spanning process development, digital process chains, and performance-driven component design. Beyond manufacturing demonstrators, we focus on structural engineering applications where WAAM delivers measurable functional value: strengthening existing steel components, repairing damaged components, and producing tailored, optimized structural details. In these scenarios, WAAM is not only a shaping technology; it also enables residual-stress and local material-property engineering, as well as local geometric tailoring to mitigate stress concentrations and improve overall performance. For that, we integrate experimental manufacturing and characterization with advanced digital modeling techniques to enable optimized, reliable, repeatable, and scalable production by developing and optimizing process parameters and deposition strategies beyond the common scope.

Our methodology integrates manufacturing, characterization, and modelling across different scales. At the process and part level, we use thermo-mechanical simulations to predict temperature fields, distortion, and residual stresses. For efficient component-scale assessment, we employ reduced-order concepts such as inherent-strain-based approaches. Where appropriate, we augment physics-informed machine learning models and data-driven surrogates to enable rapid parameter studies and uncertainty-aware process optimization.

Core research topics:

• WAAM process design (parameter optimization, deposition strategies, interlayer timing) and robustness against geometric and thermal variability
• Component-scale printing and in-situ and ex-situ process/material characterization (thermal history, melt pool shape, geometry, residual stress measurement, hardness, and mechanical testing)
• Digital models for thermal history, residual stress, and distortion prediction using different modelling approaches (e.g., Physics-informed machine learning, thermo-mechanical FE, and inherent-strain concepts)
• Application-oriented and full-scale mechanical validation, in particular for fatigue-sensitive details
• WAAM for strengthening and repair, and hybrid manufacturing concepts
• WAAM for 4D-printing of Iron-based shape memory alloy as smart details for engineering structures
• Sustainability- and cost-aware design and optimization for WAAM-enabled structural solutions
• Multimaterial and in-situ alloying concepts
• Digital twin for WAAM process

We actively collaborate with students and researchers on thesis projects and offer application-oriented pathways for industrial partners, ranging from feasibility studies to process transfer and experimental validation of WAAM components. Several master's projects on the mentioned topics are available. For more details, please contact moshayedi@stahl.uni-hannover.de

WAAM setup (to be operational from October 2026)

In recent developments, IfS has acquired a state-of-the-art WAAM setup from WAAM3D, encompassing numerous advanced functionalities for process planning, monitoring, and post-processing. IfS is prepared to utilize this system for research purposes and industrial collaboration. The primary features of the purchased system are as follows:

1) Large-scale printing

• Machine footprint: approx. 5500 × 4500 × 3600 mm (overall system).
• Build volume up to L1800 x W1200 x H1500 mm3

2) Deposition process: GMAX based on CWGMA (Cold Wire Gas Metal Arc)

• Fronius iWave 500 as the power source
• Second wire feeder with in-situ melting; Automatic 2nd wire position control
• Independent hot and cold wire feeding (material, diameter, and feed rate can be set independently), enabling a wide processing window, the highest deposition rate, multimaterial printing, and in-situ alloying.

3) Advanced end-effector and in-process sensing

• Automatic Wire Positioning System (robust wire placement during deposition).
• Pyrometer-based thermal monitoring (including front and rear pyrometers for control and documentation purposes).
• Melt pool monitoring camera for process observation and quality control.
• Real-time 3D scanning for geometry capture and process feedback.

4) Software stack for planning, simulation, and closed-loop control

• WAAM Planner: automated path planning and process-parameter suggestions based on an internal library; simulation module to support planning and validation.
• WAAM Ctrl: execution/control layer for automated deposition and monitoring.

5) Automation and adaptive process control functions

• Automatic wire-height position control (in-process adjustment).
• Automatic arc start based on surface temperature measurement.
• Shape Tech: automatic layer-height control/compensation to improve dimensional stability.

6) Post-process and inspection modules (for high-performance components)

• High-pressure rolling (HPR) inter-layer option to enhance material performance via introducing compressive residual stresses and hard working.
• Phased-array ultrasonic testing (PAUT) option for NDT aligned with WAAM/welding standards and deeper integrity assessment (e.g., residual stress measurement).

Keywords

Wire-Arc Additive Manufacturing (WAAM); Wire-Arc Directed Energy Deposition (WA-DED); DED-Arc; process planning; deposition strategy; in-situ parameter adaptation; geometry control; microstructure-property relations; anisotropy; mechanical testing; residual stresses; distortion; Finite element simulation; inherent strain method; fatigue repair and strengthening; hybrid manufacturing; sustainability; cost-aware design; topology optimization; surrogate modelling; physics-informed neural networks (PINNs).

Related-publications

E. Ghafoori, H. Moshayedi, M. Mohri, C. Diao, 9 - DED-based additive manufacturing of shape memory alloys, in: M. Mehrpouya, M. Elahinia (Eds.), Additive Manufacturing of Shape Memory Materials Additive Manufacturing Materials and Technologies, Elsevier, 2025, pp. 277–314.

H. Dahaghin, H. Moshayedi, S.M. Zahrai, M. Motavalli, E. Ghafoori, Optimization of process parameters in wire arc additive manufacturing for strengthening cracked steel plates: A thermo-mechanical study, Journal of Materials Research and Technology 39 (2025) 1311–1329. doi.org/10.1016/j.jmrt.2025.09.167.

N. Pichler, L. Li, C. Huang, D. Ferarri, M. Mohri, E. Chatzi, L. Gardner, E. Ghafoori, Effects of surface undulation on fatigue of wire arc additively manufactured ER70S-6 steel: Numerical and analytical models, Structures 80 (2025) 109878. doi.org/10.1016/j.istruc.2025.109878.

M. Ryan, M.H. Baqershahi, H. Moshayedi, E. Ghafoori, Physics-informed machine learning surrogate for scalable simulation of thermal histories during wire-arc directed energy deposition, Additive Manufacturing Letters 15 (2025) 100327. doi.org/10.1016/j.addlet.2025.100327.

M.H. Baqershahi, C. Ayas, E. Ghafoori, Topology optimisation for large-scale wire-arc directed energy deposition considering environmental impact and cost, Automation in Construction 177 (2025) 106313. doi.org/10.1016/j.autcon.2025.106313.

H. Dahaghin, M. Motavalli, H. Moshayedi, S.M. Zahrai, E. Ghafoori, Wire and arc additive manufacturing for strengthening of metallic components, Thin-Walled Structures 203 (2024) 112074. doi.org/10.1016/j.tws.2024.112074.

C. Huang, L. Li, N. Pichler, E. Ghafoori, L. Gardner, Fatigue behaviour of wire arc additively manufactured sheet material, Procedia Structural Integrity 57 (2024) 42–52. doi.org/10.1016/j.prostr.2024.03.006.

M.H. Baqershahi, C. Ayas, E. Ghafoori, Design optimisation for hybrid metal additive manufacturing for sustainable construction, Engineering Structures 301 (2024) 117355. doi.org/10.1016/j.engstruct.2023.117355.

A. Jafarabadi, I. Ferretto, M. Mohri, C. Leinenbach, E. Ghafoori, 4D printing of recoverable buckling-induced architected iron-based shape memory alloys, Materials & Design 233 (2023) 112216. doi.org/10.1016/j.matdes.2023.112216.

I.O. Felice, J. Shen, A.F. Barragan, I.A. Moura, B. Li, B. Wang, H. Khodaverdi, M. Mohri, N. Schell, E. Ghafoori, T.G. Santos, J.P. Oliveira, Wire and arc additive manufacturing of Fe-based shape memory alloys: Microstructure, mechanical and functional behavior, Materials & Design 231 (2023) 112004. doi.org/10.1016/j.matdes.2023.112004.

E. Ghafoori, H. Dahaghin, C. Diao, N. Pichler, L. Li, M. Mohri, J. Ding, S. Ganguly, S. Williams, Fatigue strengthening of damaged steel members using wire arc additive manufacturing, Engineering Structures 284 (2023) 115911. doi.org/10.1016/j.engstruct.2023.115911.

C. Huang, L. Li, N. Pichler, E. Ghafoori, L. Susmel, L. Gardner, Fatigue testing and analysis of steel plates manufactured by wire-arc directed energy deposition, Additive Manufacturing 73 (2023) 103696. doi.org/10.1016/j.addma.2023.103696.

C. Huang, Y. Zheng, T. Chen, E. Ghafoori, L. Gardner, Fatigue crack growth behaviour of wire arc additively manufactured steels, International Journal of Fatigue 173 (2023) 107705. doi.org/10.1016/j.ijfatigue.2023.107705.

N. Pichler, L. Li, C. Huang, D. Ferarri, M. Mohri, E. Chatzi, L. Gardner, E. Ghafoori, Numerical and analytical models for fatigue analysis of wire arc additively manufactured steel (2024). dx.doi.org/10.2139/ssrn.4838602