The research, published in the International Journal of Aeronautical and Space Sciences by Jaeho Lee, Chen Ciang Chia, and Jung-Ryul Lee, tackles a problem that has long frustrated automation efforts. PAUT produces enormous volumes of A-scan signals, one-dimensional traces of echo amplitude over time, and those traces are notoriously messy. Echoes bounce off the weld root, the toe, and the cap, producing geometry signals whose amplitudes can rival those of genuine defects. The initial pulse generated near the probe contaminates the beginning of every beam. Noise drifts in from the environment. To a human inspector with years of experience, these impostors are recognizable; to a neural network trained on raw signals, they are indistinguishable from cracks and porosity.

The KAIST team built their dataset from 40 single V-groove butt weld specimens, half carbon steel and half stainless steel, with thicknesses of 15 and 25 millimeters. Each specimen contained three artificially introduced flaws drawn from six categories: lack of fusion, crack, porosity, slag, undercut, and incomplete penetration. Certified ASNT Level II inspectors scanned every specimen with an Olympus OmniScan MX2, positioning two probes on opposite sides of the weld so that each defect could be observed from both directions. Crucially, the researchers then treated each side as an independent observation, so the model would learn to classify defects from single-sided data alone, a realistic constraint for field inspections where dual-sided access is not always possible. ASNT Level III experts annotated defect type, position, and beam angle, establishing ground truth as regions of interest on the C-scan images.

The first pillar of the framework is a two-stage preprocessing scheme that encodes inspector expertise directly into the data. The first stage, time-of-flight masking, exploits a simple geometric fact: defects occur inside the weld and its heat-affected zone, at a calculable distance from the weld surface. Using the probe offset, wedge delay, beam angle, and acoustic velocity stored alongside the inspection data, the team computed the spatial position of every sample in every A-scan and zeroed out anything lying more than 6 millimeters beyond the weld surface. This single operation eliminated the initial pulse and much of the ambient noise in one stroke.

The second stage is more subtle and more ingenious. Geometry signals, unlike defect signals, appear consistently at similar positions along the scan axis throughout an entire inspection, because they originate from fixed weld features rather than from localized flaws. The researchers exploited this by building a statistical threshold for every beam angle and time index. For each index, they pooled amplitudes from a three-by-three neighborhood across all scan positions, sorted the resulting distribution, fitted a line to its lower 80 percent, and then shifted that line upward by a multiple of the standard deviation. Where the shifted line crossed the sorted distribution, they set the threshold. Defect signals, being sparse and isolated, produce a low-sloped distribution with a sharp rise only at the tail, so the threshold lands high and the defect survives. Geometry signals, being densely distributed along the scan axis, produce a steeper distribution that intersects the offset line early, so nearly all their samples fall below the threshold and are erased. The team tuned the offset multiplier empirically, using a value of 30 for the louder carbon steel specimens and 8 for the quieter stainless steel ones, and extended each surviving window by 20 samples on either side to preserve the full defect waveform.

The second pillar of the framework is the neural architecture itself. Rather than simply concatenating hand-crafted features with a convolutional neural network’s output, as earlier work had done, the team inserted a feature-wise linear modulation, or FiLM, layer into the network. FiLM, originally developed for visual reasoning, works by generating per-channel scale and shift parameters from auxiliary information and applying them to intermediate feature maps. Here, three physically meaningful features computed from each A-scan, a signed distance ratio locating the echo peak relative to the weld surfaces, the peak amplitude, and the full width at half maximum of the echo, were fed through a fully connected layer to produce ten scale and ten shift values. These then modulated the ten feature channels of the CNN just before its final convolutional stage. In effect, the domain knowledge no longer waited passively at the classification head; it actively reshaped how the network perceived the raw signal.

The feature set itself was carefully revised from prior work. The team dropped the skip count, the number of back-wall reflections in the scan plan, because it reflects inspection geometry rather than defect character. They merged the binary inside-or-outside indicator with absolute distance into a single signed distance ratio normalized by the local weld width, producing a dimensionless descriptor that remains meaningful across welds of different sizes. Peak amplitude was added to capture reflection strength, which differs between strongly reflecting planar flaws and weakly reflecting volumetric ones. The six defect types were grouped into two broad classes: volumetric defects, comprising slag and porosity, and linear defects, comprising lack of fusion, cracks, incomplete penetration, and undercut. Undercut, though geometrically a surface notch, produces a sharp, narrow specular echo that behaves like a linear defect in the ultrasound data, so it joined that class.

Evaluation was deliberately stringent. Instead of randomly shuffling individual A-scans, which would let near-identical neighboring signals from the same defect leak between training and test sets and inflate performance, the team partitioned data at the level of complete single-sided inspection files, 80 in total, in tenfold cross-validation. They also trained on 196 labeled regions of interest, including normal regions deliberately covering geometry signals and noise, and addressed class imbalance with a class-weighted focal loss. The results were clear-cut. Preprocessing alone lifted mean accuracy by 6.35 percentage points and macro F1 by 10.98 points, driven largely by fewer normal signals being mistaken for defects. The FiLM layer, nearly useless without preprocessing because its input features were corrupted by noise, added a further gain when combined with it, raising volumetric defect accuracy by 8.01 percentage points, from 58.34 to 78.71 percent, the weakest class in the entire problem.

The binary confusion matrices tell perhaps the most compelling story. Without preprocessing, between 30 and 33 percent of normal signals were falsely flagged as defects, a rate that would drown any inspector in false alarms. With preprocessing, that false positive rate collapsed to 2.30 percent, while the false negative rate fell below half a percent. Merging the two defect classes into a single defect-versus-normal decision, the full framework achieved 99.26 percent accuracy, meaning the system can reliably and rapidly localize defect-bearing regions across an entire inspection before any human looks at the data.

The researchers are careful about scope. Their specimens all used a common scan plan with near-normal incidence and a single skip, and they note that extending the framework to other thicknesses, materials, groove geometries, and probe configurations, or to small-radius cylindrical welds requiring different scan strategies, will demand additional representative data. They also anticipate that larger and more varied datasets will allow finer-grained classification into more than three classes, using additional features such as depth ratio and neighboring A-scans. Even so, the study demonstrates a principle with broad implications for safety-critical industries: when deep learning is fed physics-informed preprocessing and conditioned on the same physical features experts use, it does not merely mimic inspection, it accelerates it. For aerospace maintenance, where every hour of inspection time carries real cost and every missed flaw carries real risk, an algorithm that can triage ultrasound data with expert-level reliability is not a laboratory curiosity. It is the beginning of a new division of labor, in which machines screen the flood of echoes and humans spend their expertise where it matters most.

Subject of Research: Deep learning classification of weld defects from phased-array ultrasonic testing signals

Article Title: Weld Defect Classification in Phased-Array Ultrasonic Testing Using a CNN with FiLM Layer

Article References: Lee, J., Chia, C. C., & Lee, J.-R. (2026). Weld Defect Classification in Phased-Array Ultrasonic Testing Using a CNN with FiLM Layer. International Journal of Aeronautical and Space Sciences. https://doi.org/10.1007/s42405-026-01251-2

Image Credits: AI Generated

DOI: 10.1007/s42405-026-01251-2

Keywords: phased-array ultrasonic testing, weld inspection, deep learning, convolutional neural network, FiLM layer, non-destructive testing, defect classification, aerospace safety, signal preprocessing, class imbalance, focal loss, KAIST