| Title |
Review of conventional and advanced non-destructive testing techniques for detection and characterization of small-scale defects |
| ID_Doc |
9737 |
| Authors |
Silva, MI; Malitckii, E; Santos, TG; Vilaça, P |
| Title |
Review of conventional and advanced non-destructive testing techniques for detection and characterization of small-scale defects |
| Year |
2023 |
| Published |
|
| DOI |
10.1016/j.pmatsci.2023.101155 |
| Abstract |
Inspection reliability of small-scale defects, targeting dimensions below 100 & mu;m, 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 NDTbased 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. |
| Author Keywords |
Non-destructive testing; Small-scale defects; Electromagnetism; Radiation; Ultrasonic; Hydrogen-as-a-probe; Machine learning |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:001038195000001 |
| WoS Category |
Materials Science, Multidisciplinary |
| Research Area |
Materials Science |
| PDF |
https://doi.org/10.1016/j.pmatsci.2023.101155
|