Machine-learning ultrasonic detection of fatigue cracks: a physics-based simulation study

Fatigue cracks in microtextured titanium alloys are hard to find by ultrasound. Backscatter from alpha colonies, modulated by microtextured regions, mimics a small flaw, and crack faces held in contact by residual stress reflect far less energy than their area suggests. This paper is a simulation study. It builds a forward model of pulse-echo inspection whose constants are those of mill-annealed Ti-6Al-4V, verified against closed-form solutions on 15 quantities, and uses it to characterise what a learned detector can and cannot deliver under that model. The model solves a bilinear contact law at the crack interface, coupling the linear and nonlinear scattering paths. The detector reads the drive and second-harmonic bands through a small dilated residual encoder trained on simulated records only, and converts the score into a decision by split conformal calibration, whose false-call guarantee holds under exchangeability and is not claimed under distribution shift. On simulated test specimens with crack half-lengths from 0.05 to 1.0 mm, the encoder calls 73 of 84 low-contact cracks with no false call on 180 defect-free specimens (95% upper bound 0.017), and its fitted half-length at 90% detection is 0.138 mm (bootstrap interval 0.088–0.181 mm). A second-harmonic peak with no learning has the highest part-level area under the curve, the four leading methods are not separable on that measure, and an input ablation shows that an encoder reading the second-harmonic envelope alone matches or exceeds the full model, so the harmonic band carries the performance; over five training seeds the low-contact detection count spans 73 to 82 of 84. The encoder adds decision efficiency at the operating point, nine additional low-contact detections against one lost relative to the harmonic peak (paired test p = 0.02 ), not information absent from the measurement. Detection is confined to contact fractions between 0.05 and 0.30. The decision layer alone is then applied, as a methodological case study, to measured guided-wave data from an aluminium plate cycled to 100,000 cycles, where the second-harmonic channel gives 4 false calls in 370 angle tests and the drive band 9. No experimental validation on Ti-6Al-4V or on any other titanium alloy is presented, the only measured data are aluminium, and no qualification is claimed.

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Publication Details

Journal
Frontiers in Materials
Published
2026-10-28
DOI
https://doi.org/10.3389/fmats.2026.1961699
Primary Topic
Ultrasonics and Acoustic Wave Propagation
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article
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article

Machine-learning ultrasonic detection of fatigue cracks: a physics-based simulation study

Shuo Chen, CAIFENG WANG, Shicheng Hu
Frontiers in Materials
Ultrasonics and Acoustic Wave Propagation
article

Machine-learning ultrasonic detection of fatigue cracks: a physics-based simulation study

Shuo Chen, CAIFENG WANG, Shicheng Hu
article en

Abstract

Fatigue cracks in microtextured titanium alloys are hard to find by ultrasound. Backscatter from alpha colonies, modulated by microtextured regions, mimics a small flaw, and crack faces held in contact by residual stress reflect far less energy than their area suggests. This paper is a simulation study. It builds a forward model of pulse-echo inspection whose constants are those of mill-annealed Ti-6Al-4V, verified against closed-form solutions on 15 quantities, and uses it to characterise what a learned detector can and cannot deliver under that model. The model solves a bilinear contact law at the crack interface, coupling the linear and nonlinear scattering paths. The detector reads the drive and second-harmonic bands through a small dilated residual encoder trained on simulated records only, and converts the score into a decision by split conformal calibration, whose false-call guarantee holds under exchangeability and is not claimed under distribution shift. On simulated test specimens with crack half-lengths from 0.05 to 1.0 mm, the encoder calls 73 of 84 low-contact cracks with no false call on 180 defect-free specimens (95% upper bound 0.017), and its fitted half-length at 90% detection is 0.138 mm (bootstrap interval 0.088–0.181 mm). A second-harmonic peak with no learning has the highest part-level area under the curve, the four leading methods are not separable on that measure, and an input ablation shows that an encoder reading the second-harmonic envelope alone matches or exceeds the full model, so the harmonic band carries the performance; over five training seeds the low-contact detection count spans 73 to 82 of 84. The encoder adds decision efficiency at the operating point, nine additional low-contact detections against one lost relative to the harmonic peak (paired test p = 0.02 ), not information absent from the measurement. Detection is confined to contact fractions between 0.05 and 0.30. The decision layer alone is then applied, as a methodological case study, to measured guided-wave data from an aluminium plate cycled to 100,000 cycles, where the second-harmonic channel gives 4 false calls in 370 angle tests and the drive band 9. No experimental validation on Ti-6Al-4V or on any other titanium alloy is presented, the only measured data are aluminium, and no qualification is claimed.

Frontiers in MaterialsVol. 13
Jiangsu Aviation Technical College (CN)
Openalex Percentile: Top 24%
Ultrasonics and Acoustic Wave Propagation
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