Microstructural influence on learning-based defect detection in dissimilar metal welds

Introduction Accurate defect detection in dissimilar metal welds (DMWs) remains a major challenge due to heterogeneous microstructures and imaging noise. Methods In this study, we propose a novel deep learning framework, DynaWave-Net, combined with a Guided Progressive Distillation (GPD) strategy, to address these challenges by integrating microstructural priors and frequency-domain features. The proposed model incorporates dynamic geometry-aware encoding and wavelet based attention to capture both structural deformations and high-frequency defect signatures. Results and Discussion Extensive experiments on multiple real-world datasets demonstrate that our approach significantly outperforms existing methods, achieving up to 18% improvement in precision and enhanced robustness to structural noise. Furthermore, the lightweight architecture enables real-time deployment on edge devices, highlighting the practical relevance of this work for industrial inspection in energy, aerospace, and manufacturing sectors.