Twin Sensors and Smart Controllers Keep Giant 3D-Printed Metal Walls Within Half a Millimeter

Engineers have long dreamed of printing large metal components the way a desktop printer lays down ink, but the reality of building thick, multi-layer metal walls with an electric arc has been far messier than the vision. Now, a research team reporting in the journal Advanced Materials Joining has demonstrated a closed-loop control system that keeps both the width and the height of arc-directed energy deposition (arc-DED) parts within half a millimeter of target dimensions, even after fifty layers of continuous printing. The work, led by Yuhua Cai, Dashuang Chen, Hui Chen, Guangjun Zhang, Zengxi Pan, and Jun Xiong of Southwest Jiaotong University and collaborating institutions, combines two complementary sensing streams—arc voltage and passive vision—into a single cooperative framework that watches, measures, and corrects the molten pool in real time.

Arc-DED is one of the most economical additive manufacturing routes for large metallic structures. Instead of a laser, it uses an electric arc to melt wire feedstock, achieving high deposition rates at low cost. Gas tungsten arc-DED (GTA-DED), the variant studied here, employs a non-consumable tungsten electrode and produces a smoother transition of molten material, making it attractive for high-quality fabrication in alloys ranging from aluminum to nickel and titanium. Yet the process is notoriously sensitive to disturbance. Heat accumulates layer upon layer, substrate conditions vary, and the surface state of each previously deposited bead changes the geometry the torch encounters next. When the deposited width drifts from the design value, defects such as porosity, poor fusion, and bead collapse appear between adjacent beads. When the actual height drifts from the programmed lifting height of the torch, the working distance between electrode and part degrades—too short and the tungsten electrode can collide with the surface, too long and shielding gas fails to protect the pool or the arc simply extinguishes, halting production.

Previous strategies to tame these deviations—offline heat-input models, path compensation, interlayer active cooling—share a common weakness: they do not respond to the dynamic geometry of the molten pool as it forms. Earlier sensing approaches each captured only half the picture. Infrared cameras can map pool isotherms but are expensive at the required frame rates and resolutions. Passive vision, using a CCD camera and image processing, reliably measures deposition width but struggles to track height without a side-mounted camera that collides with complex parts. Electrical sensing of arc voltage, by contrast, cheaply and almost instantaneously reflects the distance from torch to deposition surface—effectively the height—but says nothing about width. The innovation of the new study is to fuse the two: arc voltage for height, vision for width, each sampled at the moment it performs best.

Raw arc voltage signals, however, are noisy. Fluctuations in the conductive channel of the arc plasma corrupt the measurement, and classical threshold-based wavelet filters, derived under idealized Gaussian white-noise assumptions, either over-filter and erase faint signal features or leave too much residual noise. The team’s answer was to let an ant colony optimization algorithm search for the optimal wavelet threshold. Inspired by the pheromone trails ants lay to find shortest paths, the algorithm iteratively updates pheromone concentrations across candidate thresholds, guided by the mean squared error of the filtered signal. Because the search is global rather than fixed by a closed-form expression, the filter adapts to the actual character of each signal, preserving transient peaks and abrupt changes that carry real physical information about deposition height stability.

Calibration posed a subtler challenge. In a thick-walled, multi-layer, multi-bead part, the arc does not look the same everywhere. On the first layer, heat dissipates efficiently into the substrate and no closed-loop control is needed. On the second layer, the arc straddles the first layer’s surface and the substrate. By the third layer, the arc touches the previous layer’s surface and side wall, and from the second bead onward it spans two deposited layers simultaneously. Each morphology produces distinct electrical signatures, so a single arc-length model would be hopelessly inaccurate. The researchers therefore constructed three separate plane-fitting models linking peak arc voltage, arc length, and peak current—one for the first bead of the second layer, one for the first bead of the third layer, and one for subsequent beads—and deployed the appropriate model as deposition progressed.

Vision sensing required equally careful choreography. The system uses pulsed current, and at peak current the arc blazes so brightly that it saturates the camera’s dynamic range, drowning the molten pool in glare and reflections from neighboring beads. At base current, arc-light interference drops dramatically and image quality improves. The team therefore timed every image capture to the eightieth millisecond after the current transitioned from base to peak, using a camera fitted with a 25 mm lens, a 2 percent neutral density filter, and a narrow-band 685 nm filter. Image processing proceeded through Gaussian filtering to suppress noise, a Laplacian operator to detect pool edges, and a Hough transform to extract them, with two small 50-by-380-pixel windows positioned at the tail of the molten pool—far enough from the arc glare at the pool head, yet not so far back that solidification introduced lag. A chessboard calibration, cross-referenced against the arc-voltage-derived arc length, converted pixel coordinates into millimeters, yielding a width monitoring error below 0.04 mm.

With sensing in place, the control architecture split the problem in two. A model reference adaptive controller (MRAC) regulated wire feeding speed to stabilize deposition height: a PID controller computed wire-speed corrections from the deviation between detected and reference arc voltage, while an adaptation mechanism continuously retuned the PID gains based on sensitivity functions, allowing the controller to track a time-varying nonlinear process. In parallel, a self-tuning fuzzy controller (FSTC) adjusted peak current to hold the pool width at target. Rather than fixing the fuzzy controller’s quantization and scale factors in advance, the FSTC recalculated them on the fly from the width error and its rate of change, following rules distilled from expert experience and requiring no precise quantitative model of the process. Simulation of the width controller showed that when the target pool width jumped from 5 to 5.5 mm, the system tracked the change within five seconds without significant overshoot.

The experimental contrast was striking. With constant process parameters, a ten-layer, ten-bead thick-walled part of ER70S-6 steel wire on Q235B substrate grew increasingly erratic: peak arc voltage climbed layer by layer as height deviations accumulated, marginal beads deposited taller than interior beads, and the pool width drifted from 4.5 to about 5.3 mm as heat dissipation conditions evolved. By the eleventh layer, the tungsten-to-substrate distance exceeded the maximum at which an arc could be initiated, and the process simply stopped. Dents pocked the termination region, fusion lines between beads were irregular, and the finished part showed misalignment and bulging, with a height deviation reaching 5 mm across the part.

Under closed-loop control, the same system printed a fifty-layer, ten-bead part continuously and without manual intervention. The MRAC held arc voltage deviations to a maximum of roughly 0.27 to 0.31 volts during stable burning, while the FSTC pinned the molten pool width near 4.5 mm regardless of layer number, with a maximum absolute width error no greater than 0.45 mm and a mean squared error below 0.25 mm². Three-dimensional scanning of the finished components told the definitive story: height deviations of only 0.3 mm after fifty layers and width deviations of just 0.1 mm, compared with 5 mm and 0.7 mm respectively for the open-loop part. The top surface was smooth, bead-to-bead consistency within each layer was excellent, and the dents and defects that plagued constant-parameter printing vanished.

The authors are candid about limits. The filtering algorithm currently applies only to direct-current arc voltage signals in steel; highly reflective metals such as aluminum, typically welded with alternating current, will demand new denoising and vision algorithms to suppress noise and glare. The demonstration parts were simple cubes, and extending closed-loop control to curved geometries will strain the arc-length models, since curved trajectories continuously shift local heat accumulation, wire position, and arc shape. Still, the strategy offers a practical path forward: as a data-driven software solution, it can be integrated into existing industrial control systems through modest secondary development, without expensive hardware replacement or production downtime. For an industry seeking to print large, accurate metal parts at low cost, two humble sensors and a pair of cleverly tuned controllers may prove to be the difference between a promising laboratory process and a factory-floor reality.

Subject of Research: Collaborative closed-loop control of deposition width and height in gas tungsten arc-directed energy deposition using integrated arc voltage and vision sensing

Article Title: Collaborative control of deposition width and height for thick-walled parts in arc-directed energy deposition via integrating arc voltage and vision sensing

Article References: Cai, Y., Chen, D., Chen, H., Zhang, G., Pan, Z., & Xiong, J. (2026). Collaborative control of deposition width and height for thick-walled parts in arc-directed energy deposition via integrating arc voltage and vision sensing. Advanced Materials Joining, 1(1), Article 15. https://doi.org/10.1007/s44500-026-00017-w

Image Credits: AI Generated

DOI: 10.1007/s44500-026-00017-w

Keywords: arc-directed energy deposition, GTA-DED, additive manufacturing, arc voltage sensing, vision sensing, closed-loop control, model reference adaptive control, fuzzy control, molten pool monitoring, thick-walled parts, wire arc additive manufacturing, forming accuracy