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Models of Printed Circuit Boards Conductive Pattern Defects
Author(s) -
Evgeniya Danilova,
И. И. Кочегаров,
N. К. Yurkov,
Mikhail Miheev,
Normunds Kante
Publication year - 2018
Publication title -
applied computer systems
Language(s) - English
Resource type - Journals
eISSN - 2255-8691
pISSN - 2255-8683
DOI - 10.2478/acss-2018-0016
Subject(s) - computer science , software , printed circuit board , reliability engineering , identification (biology) , stress (linguistics) , imitation , development (topology) , engineering , mathematics , social psychology , psychology , biology , philosophy , mathematical analysis , programming language , linguistics , botany , operating system
A number of PCB defects, though having passed successfully the defect identification procedure, can potentially grow into critical defects under the influence of various external and (or) internal influences. The complex nature of the development of defects leading to PCB failures demands developing and updating the data measuring systems not only for detection but also for the prediction of future development of PCB defects considering the external influences. To solve this problem, it is necessary to analyse the models of defect development, which will allow predicting the defect growth and working out the mathematical models for their studies. The study uses the methods of system analysis, theory of mathematical and imitation modelling, analysis of technological systems. The article presents four models for determining the theoretical stress concentration factor for several types of common defects, considering the strength loss of PCB elements. For each model the evaluation of parameters determining its quality is also given. The formulas are given that link the geometry of defects and the stress concentration factor, corresponding to four types of defects. These formulas are necessary for determining the number of cycles and time to failure, fatigue strength coefficient. The chosen models for determining the values of the stress concentration factor can be used as a database for identifying PCB defects. The proposed models are used for software implementation of the optical image inspection systems.

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