Quantitative evaluation of dislocation density in SUS316L utilizing non-contact microwave reflection method

Abstract

Metallic materials are widely employed due to their exceptional mechanical attributes. Plastic deformation is a common issue that influences both the mechanical and electrical properties, with profound implications for the longevity of engineered structures and components. Dislocation density is the core factor in the plastic deformation process. Traditional methods for the evaluation of dislocation density include electron backscatter diffraction (EBSD), transmission electron microscopy (TEM), and x-ray diffraction (XRD), requiring meticulous specimen preparation and sophisticated equipment posing challenges for industry-wide fitness-for-service (FFS) monitoring. To address these challenges, a non-contact method to quantitatively evaluate the dislocation density in stainless steel 316L (SUS316L) by employing a microwave reflection method is reported in this study. A dedicated microwave measurement system, coupled with a coaxial line sensor, was used to measure the amplitude of the reflection coefficient of the microwave signal at a constant standoff distance and frequency, closely associated with the electrical resistivity of the SUS316L specimens, a parameter that exhibits variation in response to changes in dislocation density. The results indicate a proportional increase in the amplitude of the microwave signal with higher dislocation density. By establishing a linear correlation, we demonstrate the feasibility of evaluating dislocation density with a minimum detectable difference of 1.824 × 1013 m-2 using the dedicated microwave system under the designed conditions in this study. This approach holds promise for better understanding and monitoring of plastic deformation in metallic materials across various applications.

Keywords

• plastic strain
• dislocation density
• non-destructive testing
• x-ray diffraction (xrd)
• electron backscatter diffraction (ebsd)
• electrical resistivity
• microwave