Mission-critical electronic systems demand early and accurate detection of solder joint degradation to ensure reliability. Quad Flat No-Lead (QFN) packages, widely used in automotive and industrial applications, are especially prone to vibration-induced solder fatigue. However, traditional failure analysis methods (e.g., dye-and-pry, cross-sectioning, manual Xray inspection) are labor-intensive and often insufficient to detect early-stage cracks. This paper presents an automated inspection framework that combines high-resolution 3D X-ray tomography with a YOLOv11-based deep learning model to detect and segment vibration-induced cracks in QFN solder joints. The pipeline achieves precise localization of cracks in volumetric data, discriminates them from voids, and extracts morphological descriptors through parametric fitting. By statistically correlating these image-derived crack features with electrical resistance measurements recorded in situ during vibration tests, we establish a direct link between physical crack evolution and functional degradation of the joint. The results demonstrate that our AI-driven method can automatically identify tiny solder cracks and reliably offer predict impending interconnect failures in comparable granularity of traditional inspection techniques, surpassing them in speed. This approach offers a powerful prognostic health monitoring tool for electronic packaging, and it is extensible to other package types and stress conditions.

We present a new numerical simulation framework for prediction of flow patterns in the human left ventricle model. In this study, a radial basis function (RBF) mesh morphing method is developed and applied within the finite-volume computational fluid dynamics (CFD) approach. The numerical simulations are designed to closely mimic details of recent tomographic particle image velocimetry (TomoPIV) experiments. The numerically simulated dynamic motions of the left ventricle and tri-leaflet biological mitral valve are emulated through the RBF morphing method. The arbitrary Lagrangian-Eulerian (ALE) based CFD is performed with the RBF-defined deforming wall boundaries. The results obtained show a good agreement with experiments, confirming the reliability and accuracy of the developed simulation framework.

Mitral valve (MV) leaflets affect the formation, growth, and decay of vortices in the left ventricle (LV) during diastolic filling. The shape and motion of MV leaflets are simplified in most studies due to computational restrictions. In this study, we present a newly developed mathematical method to model the dynamic movement of valve leaflets and annulus, which is based on in vivo data obtained with magnetic resonance imaging (MRI). In the present method, we solve a boundary value problem where the MV surface is initially unknown. The resultant MV shapes are included in a dynamic motion model of the LV to assess the change of intraventricular flow patterns. To estimate the effects of the MV on left intraventricular flow, a LV model without MV leaflets was also simulated for comparison. Our study showed that the presence of the MV and the shape of its leaflets significantly altered the formation and evolution of vortex structures in the LV. The various MV leaflet shapes accelerate the transvalvular flow distinctly, leading to different formation and development of vortex structures.