Computer Aided System for Inspection of Assembled PCB

Nahar, Jinay. A.; Phadke, Anuradha C.

doi:10.1109/iccs45141.2019.9065636

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…The subtracted image is compared to the original PCB design and computes the location of the missing component by classifying it as missing. Jinay Nahar, et al [3], propose an algorithm to inspect faults in assembled PCBs by using image subtraction. The subtracted image is converted to a grayscale image and is passed to an algorithm that determines the faulty regions and classifies them accordingly.…”

Section: B Literature Surveymentioning

confidence: 99%

“…The current techniques used for finding missing components only use image-processing. Many researchers [2,3,6] present solutions that use image subtraction to carry out missing component detection along with some other image-processing techniques. Whereas, PCB-Fire detects the missing components and classifies them using a combination of deep learning, image subtraction and pixel manipulation as its foundation as compared to the method proposed by Y. Lin, et al [9], where only the types of capacitor is being detected.…”

Section: A Comparison With Existing Solutionsmentioning

confidence: 99%

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PCB-Fire: Automated Classification and Fault Detection in PCB

Khare

Bahel

Phadke

2020

2020 Third International Conference on Multimedia Processing, Communication &Amp; Information Technology (MPCIT)

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Printed Circuit Boards ("PCB") are the foundation for the functioning of any electronic device, and therefore are an essential component for various industries such as automobile, communication, computation, etc. However, one of the challenges faced by the PCB manufacturers in the process of manufacturing of the PCBs is the faulty placement of its components including missing components. In the present scenario the infrastructure required to ensure adequate quality of the PCB requires a lot of time and effort. The authors present a novel solution for detecting missing components and classifying them in a resourceful manner. The presented algorithm focuses on pixel theory and object detection, which has been used in combination to optimize the results from the given dataset.

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Section: B Literature Surveymentioning

confidence: 99%

Section: A Comparison With Existing Solutionsmentioning

confidence: 99%

PCB-Fire: Automated Classification and Fault Detection in PCB

Khare

Bahel

Phadke

2020

2020 Third International Conference on Multimedia Processing, Communication &Amp; Information Technology (MPCIT)

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Automatic printed circuit board inspection: a comprehensible survey

Fonseca,

Iano,

Oliveira

et al. 2024

Discov Artif Intell

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The printed circuit board (PCB) plays a critical role in any electronic product, and its manufacturing quality assurance is responsible for substantially impacting the final product’s price. Hence, research and development (R&D) for better inspection methods have been an internationally growing subject. Regarding the multiple PCB inspection paradigms, the visual investigation of these boards represents the majority of modern techniques. Thus, the present survey collects the most impacting studies over the last 25 years, highlighting their operation strategies and how they evolved until recently, including introducing artificial intelligence (AI) approaches to increase their overall performance and lower operating costs. Furthermore, this work calls attention to the importance of public PCB datasets to contribute to developing such methods and provide benchmarking references. Finally, the most recent challenges in the field are described and discussed.

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