A Systematic Analysis of Printed Circuit Boards Bending during In-Circuit Tests

Abstract

When performing In-Circuit Tests (ICTs) of Printed Circuit Boards (PCBs), there are certain phenomena related with strain analysis that must be known in order to obtain stronger and more accurate testing results. During testing, PCBs are often subjected to mechanical bending efforts that induce excessive strain. This study focuses on the building of a Finite Elements Analysis (FEA) methodology that prevents excessive bending strain in critical points of a PCB during an ICT. To validate this methodology, a set of experimental tests, matched with a set of FEA, were carried out. Thus, companies, before the development of an ICT machine (fixture), will be able to use this FEA methodology to predict whether the maximum strain of a PCB under study, when subjected to its ICT, will damage it, thus reducing unnecessary production costs. A guideline was thus designed to enable the creation of the most representative Finite Elements Model (FEM) for any PCB, based on its amount and direction of copper traces.

Printed circuit board and printed circuit board assembly methods for testing and visual inspection: a review

“…Oliveira et al [24] propose a methodology to prevent excessive bending strain in important PCB's points when in-circuit test is performed. It is based on finite element analysis and is verified through experimentation.…”

“…− Risk problems to be identified during design of the electronic product − Failure issues to be removed Li et al [23] − Sensor identified changes in frequency or magnitude − Non-destructive − Defects detection on metal surface − Locate notch damages, their dimension, and orientation Oliveira et al [24] − Finite element analysis − To prevent excessive bending strain in important PCB's points when in-circuit test is performed − PCB maximal strain at in-circuit test to be predicted − To understand whether such test could damage PCB Volkau et al [25] − Unsupervised deep learning and transfer learning − Detecting PCB defects on images (scratch, broken PCB edge, and hole) Nguyen and Bui [26] − Algorithms for feature extraction from images and supervised deep learning − PCB defect detection − Visual inspection in real time Silva et al [27] − Transfer learning and VGG16/resnet-50 pre-trained models − Identifying the defective PCBs − Non-referential inspection…”