Abstract
Inspection reliability of small-scale defects, targeting dimensions below 100 µm, is crucial for structural safety of critical components in high-value applications. Early defects are often possible to repair, contributing for the circular economy and sustainability by allowing extended life and reuse of components. During in-service operation, the small-scale defects are typically originated from creep, fatigue, thermal cycles, and environmental damage, or any combination of these multiphysical loading conditions. What are thresholds in Non-Destructive Testing (NDT) techniques to detect and reliably characterise small-scale defects? What is the state of the art of NDT-based solutions, in terms of small-scale defects located at surface, and interior of materials? Examples of small-scale defects in engineering materials are established, and a holistic review is composed on the detectability in terms of sensitivity and resolution. Distinguishable high detection accuracy and resolution is provided by computed tomography paired with computer laminography, scanning thermal microscopy paired with Raman spectroscopy, and NDT techniques paired with machine learning and advanced post-processing signal algorithms. Other promising techniques are time-of-flight diffraction, thermoreflectance thermal imaging, advanced eddy currents probes, like the IOnic probe, micro magnetic bridge probe used in magnetic flux leakage, driven-bacterial cells, Quantum dots and hydrogen-as-a-probe.
Original language | English |
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Article number | 101155 |
Journal | Progress in Materials Science |
Volume | 138 |
DOIs | |
Publication status | Published - Sept 2023 |
MoE publication type | A2 Review article in a scientific journal |
Funding
This research was funded by the Academy of Finland, via project no. 325108 (New high-resolution non-destructive methods for assessment of early damage in advanced welded steels for high-temperature applications with extended life: EARLY), and by the Portuguese Fundação para a Ciência e a Tecnologia (FCT - MCTES), via the projects UIDB/00667/2020 and UIDP/00667/2020 (UNIDEMI). The authors would like to thank the Academy of Finland for its financial support via project 325108 and Fundação para a Ciência e a Tecnologia (FCT - MCTES) for its financial support via the projects UIDB/00667/2020 and UIDP/00667/2020 (UNIDEMI).
Keywords
- Electromagnetism
- Hydrogen-as-a-probe
- Machine learning
- Non-destructive testing
- Radiation
- Small-scale defects
- Ultrasonic