Enhancement of Natural Ventilation using Solar Chimney: A Numerical Investigation

Kumar, J.; Raj, Abhishek; Sharma, Hari

doi:10.22161/ijaers.4.3.40

Cited by 2 publications

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Mukesh Kumar and colleagues [1] use an image enhancement algorithm to sharpen the edges of tracks in PCB, filter out the noise effectively with the help of National Instrument Vision assistant software. The average time ready to perform the proposed algorithm is 10 ms or 99.43 parts inspected per second T. J. Mateo Sanguino et al [2] The visual inspection system is used for automatic detection and classification for finding errors.…”

Section: Literature Surveymentioning

confidence: 99%

Automated Inspection System for Assembled Printed Circuit Board Using Machine Vision

Goupale¹,

Gyanchandani²,

Chauhan³

et al. 2023

Artificial Intelligence and Communication Technologies

View full text Add to dashboard Cite

The perfect Printed Circuit Board (PCB) plays a very important role in every electronic device as well as in automation systems. So, it is very important to find defects in the PCB before installing it to any system or any device. However, PCB Manufacturers use various inspection systems in the process of manufacturing PCBs for detecting various types of defects in the PCB. In this article, we present the Automated assembled PCB Inspection System. This system finds defects such as missing components and improper position of its components by using the Pattern matching Technique where a good known score of template image is matched with the score of the test image. This system gives results at each inspection within 10 Seconds and the result given by this system are passed or fail in the form of an array sheet. This automated inspection system is created by using NI Vision Builder AI and NI LabVIEW technology. Ni Vision Builder AI has been used to create the algorithm. And NI LabVIEW has been used to create the application.

show abstract

Section: Literature Surveymentioning

confidence: 99%