Zhongqing Zhang¹, Bo Wan^1,*, Guicui Fu¹, Yutai Su^2,*, Zhaoxi Wu³, Xiangfen Wang¹, Xu Long²

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 441-458, 2024, DOI:10.32604/cmes.2023.043616

Abstract Sintered silver nanoparticles (AgNPs) are widely used in high-power electronics due to their exceptional properties. However, the material reliability is significantly affected by various microscopic defects. In this work, the three primary micro-defect types at potential stress concentrations in sintered AgNPs are identified, categorized, and quantified. Molecular dynamics (MD) simulations are employed to observe the failure evolution of different microscopic defects. The dominant mechanisms responsible for this evolution are dislocation nucleation and dislocation motion. At the same time, this paper clarifies the quantitative relationship between the tensile strain amount and the failure mechanism transitions of the three defect types by… More >

Jakkrit Onshaunjit, Jakkree Srinonchat^*

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 355-367, 2022, DOI:10.32604/cmc.2022.017698

Abstract An ideal printed circuit board (PCB) defect inspection system can detect defects and classify PCB defect types. Existing defect inspection technologies can identify defects but fail to classify all PCB defect types. This research thus proposes an algorithmic scheme that can detect and categorize all 14-known PCB defect types. In the proposed algorithmic scheme, fuzzy c-means clustering is used for image segmentation via image subtraction prior to defect detection. Arithmetic and logic operations, the circle hough transform (CHT), morphological reconstruction (MR), and connected component labeling (CCL) are used in defect classification. The algorithmic scheme achieves 100% defect detection and 99.05%… More >