Treffer: Development of a novel deleterious phases analysis and detection method for stainless steel.

The integrity and performance of stainless steel can be significantly impacted by thermal aging, leading to the formation of deleterious phases such as sigma and chi phase. These deleterious phases, which are often enriched in chromium and molybdenum, affect the steel's mechanical properties by reducing ductility and toughness. Traditional methods for detecting and analysing these phases are either not consistent, costly, or lack the precision necessary for a comprehensive analysis. This study introduces a novel Python based method for the qualitative and quantitative analysis of deleterious phases. This method is applied to thermally aged 316 L stainless steel, utilizing advanced image processing techniques, including Contrast Limited Adaptive Histogram Equalization (CLAHE) and automated particle detection algorithms, this method analyses Scanning Electron Microscopy (SEM) images to identify bright particles as an indication of phase changes. The methodology is capable of processing both individual images and batches of micrographs, providing detailed statistics on the number of particles, area percentages, average aspect ratio, circularity, particle density, spatial distribution metrics and additional particle statistics. Results showed that the area percentage of deleterious phases increased with decreasing aging temperature and increasing time, ranging from 2 % to 13.43 %. The proposed method's reliability was validated against X-ray diffraction and ImageJ software demonstrating its accuracy and sensitivity. By offering precise, scalable, and user-friendly quantitative analyses, this methodology represents a valuable tool for industrial quality control, material optimization, and life prediction of stainless-steel components, particularly in critical energy applications. [Display omitted] • A Python-based pipeline is developed for automated microstructure analysis. • The method detects and quantifies σ and χ phases in SEM micrographs. • Particle shape, size, and spatial metrics are extracted across aging conditions. • CLAHE-enhanced preprocessing improves contrast and segmentation accuracy. • Results are benchmarked against XRD and ImageJ, showing improved scalability and reproducibility. [ABSTRACT FROM AUTHOR]