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<p>This study addresses corrosion fatigue inspection of laser-welded 316L stainless steel plates for marine infrastructure that operates in chloride environments. An in situ testbed combines scattered-light roughness sensing, stereo digital image correlation, and electrochemical impedance spectroscopy during tensile fatigue in 3.5% NaCl to track the evolution of surface damage, strain localization, and electrochemical response under cyclic stresses around ± 500&#xa0;MPa at ± 1% strain. Roughness metrics <i>A</i><sub><i>q</i></sub> and <i>ΔA</i><sub><i>q</i></sub> grow monotonically with load cycles and yield 90% probability-of-detection thresholds of 0.18 for the base metal, 0.14 for the heat-affected zone, and 0.11 for the fusion zone. Charge-transfer resistance falls from 180 to 40&#xa0;Ω cm<sup>2</sup> as damage accumulates. A machine learning classifier that fuses scattered light, digital image correlation, and electrochemical impedance spectroscopy features achieves area under the curve of 0.97, accuracy of 92.5%, and recall of 95%, with probability of detection close to 0.9 for crack sizes around 0.8&#xa0;mm. The work establishes fused scattered light, digital image correlation, and electrochemical impedance spectroscopy as a quantitative framework for early detection of corrosion fatigue damage in welded 316L under aggressive saline loading.</p>

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Fused Optical Mechanical and Electrochemical Sensing for Early Corrosion Fatigue Detection in Welded 316L

  • 期刊论文

  • 发表日期：

    2026年6月20日

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  • Q. Alkhawlani,
  • Sari Khdhaer Mukhlif,
  • Salah Sabeeh,
  • Sameer Algburi,
  • Abdullah Faiz Al Asmari,
  • Saiful Islam

摘要

This study addresses corrosion fatigue inspection of laser-welded 316L stainless steel plates for marine infrastructure that operates in chloride environments. An in situ testbed combines scattered-light roughness sensing, stereo digital image correlation, and electrochemical impedance spectroscopy during tensile fatigue in 3.5% NaCl to track the evolution of surface damage, strain localization, and electrochemical response under cyclic stresses around ± 500 MPa at ± 1% strain. Roughness metrics Aq and ΔAq grow monotonically with load cycles and yield 90% probability-of-detection thresholds of 0.18 for the base metal, 0.14 for the heat-affected zone, and 0.11 for the fusion zone. Charge-transfer resistance falls from 180 to 40 Ω cm2 as damage accumulates. A machine learning classifier that fuses scattered light, digital image correlation, and electrochemical impedance spectroscopy features achieves area under the curve of 0.97, accuracy of 92.5%, and recall of 95%, with probability of detection close to 0.9 for crack sizes around 0.8 mm. The work establishes fused scattered light, digital image correlation, and electrochemical impedance spectroscopy as a quantitative framework for early detection of corrosion fatigue damage in welded 316L under aggressive saline loading.