What question did this study set out to answer?

To review the evolution of monitoring techniques in Wire Arc Additive Manufacturing, highlighting machine learning applications.

May 2, 2026Discover Applied Sciences0 citationsOpen Access

Visual and thermal monitoring techniques for Wire Arc Additive Manufacturing: from conventional to machine learning approaches

MGMaedeh GourakaniInstitute for High Performance Computing and Networking CMCarmelo MineoInstitute for High Performance Computing and Networking DCDonatella CernigliaUniversity of Palermo

Key Points

To review the evolution of monitoring techniques in Wire Arc Additive Manufacturing, highlighting machine learning applications.
Overview of vision and thermal monitoring methods up to machine learning approaches.
Discussion of sensor technologies, measurable parameters, and traditional analysis techniques.
Examined machine learning methods including classification, detection, and physics-informed learning.
Identified gaps in robust sensing and data scarcity in arc environments.
Highlighted the potential of machine learning to enhance process monitoring.
Discussed the importance of integrating intelligent monitoring with control strategies.

Abstract

Abstract Wire Arc Additive Manufacturing (WAAM) offers high deposition rates and low feedstock cost, but process variability can cause defects, residual stresses, and geometric errors. No single sensor captures the full process state, motivating multimodal monitoring. This review traces progress from vision- or thermal-only monitoring to machine-learning approaches for vision–thermal fusion. We summarise sensing hardware and measurable parameters; outline traditional image/thermal analyses; review recent ML methods for unimodal data (classification, detection, transfer learning, physics-informed learning); and discuss fusion architectures (early/mid/late), including their synchronisation assumptions and practical guidance for trigger-based versus learned alignment. We then connect intelligent monitoring to control strategies (inter-layer, intra-layer, and hybrid), highlighting opportunities for model-predictive and adaptive control. Open challenges include robust sensing in arc environments, sim-to-real fidelity, data scarcity, and generalisation across materials and setups. Overall, integrating complementary sensors with ML can improve process interpretation and control, supporting the transition toward more autonomous WAAM.

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