In a study published in Frontiers in Manufacturing Technology, engineers from Cranfield’s Centre for Computational Engineering Sciences, the Welding and Additive Manufacturing Centre, and the Centre for Robotics and Assembly unveiled WAAM-ViD, a vision-based monitoring pipeline that can analyse the melt pool in wire-based directed energy deposition regardless of where the camera sits, what kind of camera is used, or how the lighting conditions change. The work addresses one of the most stubborn limitations in additive manufacturing quality control: the tendency of monitoring systems to be locked to a single machine, a single camera position, and a single set of optical assumptions, making them nearly impossible to transfer between industrial environments.

WAAM itself is formally classified under the ISO/ASTM 52900 standard as directed energy deposition using an arc, or DED-Arc. It works by harnessing conventional welding technology, using a plasma or electric arc torch to melt metal wire feedstock and deposit it in layers. The appeal is obvious: deposition rates are high, raw material is cheap wire rather than expensive powder, and the technique scales to components measured in metres rather than millimetres. Powder bed fusion, by contrast, offers exquisite precision for small, intricate parts but cannot match WAAM’s throughput for large structures. That is why aerospace manufacturers, who need both complexity and scale, have been among the earliest and most enthusiastic adopters of metal additive manufacturing in general and WAAM in particular.

The challenge is that WAAM is exquisitely sensitive to its own process parameters. The researchers identify three primary sources of defects: material cleanliness, where impurities in the feedstock wire promote porosity and internal flaws; material reactions, where the interaction between wire and arc produces cracking and microstructural irregularities during solidification; and process instability, which manifests as humping, spatter, overlapping, collapse, and machine suspension. Internal defects often require advanced non-destructive techniques such as computed tomography to detect after the fact. Process instabilities, however, can often be observed in real time, which is precisely where vision-based monitoring earns its keep. A camera watching the melt pool can catch anomalies the instant they form, rather than discovering them in a finished part weeks later.

Previous approaches to vision-based WAAM monitoring have achieved impressive results but with significant caveats. Some studies positioned cameras behind the welding torch to minimise arc glare, achieving defect classification accuracies above 95 percent, but this rear-mounted configuration introduces a fundamental delay: defects are only observed after deposition has already occurred. Others focused directly on the melt pool using high dynamic range cameras and narrowband optical filters to suppress the intense arc light, training deep learning models such as ResNet and ResNeXt to classify defects with accuracies approaching 99 percent. Yet when researchers attempted actual dimensional analysis of the melt pool, they ran into a wall of hardware dependency. Threshold-based segmentation, the standard technique for isolating the melt pool in an image, is notoriously sensitive to lighting variations and arc interference. Systems calibrated on one camera at one angle simply failed when moved.

The Cranfield team’s answer is a two-stage deep learning pipeline that decouples the monitoring problem from any specific imaging setup. The first stage uses DeepLabv3, a semantic segmentation architecture built on a ResNet-50 backbone, to identify and isolate the melt pool in each video frame. Rather than training on a fully labelled dataset, which would have required enormous annotation effort, the researchers employed an active learning paradigm: the model was initially trained on limited labelled data, then iteratively fine-tuned on the frames where it was least confident, as measured by Shannon entropy. Four independent raters assessed segmentation quality using a structured assessment form, and videos with the highest uncertainty and lowest qualitative scores were prioritised for annotation in subsequent training cycles. The process converged after five cycles, with the fourth cycle’s parameters delivering the best performance: a median Dice similarity coefficient of 95.90 percent, a standard metric of overlap between predicted and ground-truth segmentation masks.

The second stage is where the innovation becomes genuinely distinctive. WAAM-ViDNet, a regression-based multimodal model, takes the segmented melt pool image and fuses it with camera calibration metadata, including the camera matrix, distortion coefficients, rotation matrix, and translation vector extracted for each viewing angle. This allows the model to translate what it sees in pixel space into physically meaningful dimensions in millimetres, regardless of perspective. The architecture uses AlexNet as its feature extractor, chosen for its proven effectiveness and lightweight structure, with features concatenated with calibration data and passed through fully connected layers to predict melt pool width. Among five candidate configurations tested, the researchers selected a variant with five fully connected layers that achieved 88.71 percent accuracy while keeping computational burden low enough for real-time deployment.

To train and validate this system, the team built something the field has lacked entirely: a public benchmark dataset. WAAM-ViD comprises 110 videos with an average duration of 26.5 seconds, captured at 25 to 30 frames per second using two fundamentally different cameras. Eighty videos came from a passive Xiris XVC-1000 high dynamic range CMOS camera, which boasts a dynamic range exceeding 140 decibels and can image directly into the arc’s glare. Thirty came from a CAVITAR C400-H active laser illumination camera, which uses a narrowband pulsed laser to suppress arc light interference entirely. Cameras were positioned at five orientations relative to the plasma torch, including top-backwards, top-central, top-forward, side-central, and side-forward, with angle and distance systematically varied. Ground truth weld bead dimensions were measured post-process using a Micro-Epsilon laser profile sensor with 1.5-micrometre resolution, then temporally aligned with each video frame. The dataset, complete with manually annotated segmentation masks in COCO JSON format and full metadata, is now publicly available.

The results reveal both the promise and the honest limitations of the approach. Overall prediction accuracy of 88.71 percent is lower than the 97 to 99 percent figures reported by studies using single, optimally positioned cameras with unobstructed top-down views of the melt pool. But the Cranfield researchers argue this comparison misses the point: their system was evaluated across multiple camera viewpoints with varying occlusions, a substantially harder learning task that no prior study had attempted. Performance varied between 9.43 and 13.23 percent error depending on orientation, with the best results from the top-forward HDR configuration and the worst from side orientations with the least training data. Inference speed is remarkable: the full pipeline, segmentation plus width prediction, runs in 0.014 seconds per frame on an Nvidia RTX 4070 Ti Super GPU, fast enough for genuine real-time monitoring, though the researchers caution that performance would degrade on less powerful hardware.

The researchers are candid that their work is a foundation rather than a finished product. The current post-processing approach, which retains only the largest connected segmentation mask, risks suppressing the true melt pool if noise happens to be larger. The dataset contains only normal operating conditions, without simulated defects such as humping, and was collected on a single system with a single alloy, ER70S-6 steel. The authors also point toward Industry 5.0, arguing that explainability and trustworthiness will be essential for human-machine collaboration, and that uncertainty quantification, which they explored for active learning, deserves deeper investigation as a tool for building operator confidence. Still, the direction is clear. By demonstrating that melt pool analysis can survive changes in camera angle, quality, and illumination, and by releasing the data and code to prove it, the Cranfield team has laid the groundwork for monitoring systems that travel between factories rather than being welded, figuratively speaking, to a single machine. For a manufacturing technology whose economic case depends on producing large, safety-critical components reliably, that kind of portability may prove to be the difference between WAAM remaining a laboratory curiosity and becoming a mainstay of industrial production.

Subject of Research: Angle-invariant vision-based melt pool monitoring for wire arc additive manufacturing using deep learning

Article Title: WAAM-ViD: towards universal vision-based monitoring for wire arc additive manufacturing

Article References: Kim, K. W., Kamerkar, A., Chiu, T.-E., Abdi, I., Qin, J., Suder, W., & Asif, S. (2025). WAAM-ViD: towards universal vision-based monitoring for wire arc additive manufacturing. Frontiers in Manufacturing Technology, 5, Article 1676365. https://doi.org/10.3389/fmtec.2025.1676365

Image Credits: AI Generated

DOI: 10.3389/fmtec.2025.1676365

Keywords: wire arc additive manufacturing, WAAM, melt pool monitoring, deep learning, semantic segmentation, computer vision, directed energy deposition, additive manufacturing, benchmark dataset, active learning, Industry 4.0, quality control