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Automated detection of Printed Circuit Boards (PCB) defects by using machine learning in electronic manufacturing: current approaches

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cris.virtual.department Universiti Malaysia Perlis
cris.virtual.department Universiti Malaysia Perlis
cris.virtualsource.department e4aaaf69-3436-4dcd-ab15-19828fa5a557
cris.virtualsource.department cd3b8814-1940-43af-8811-eda440737492
dc.contributor.author S. S. Zakaria
dc.contributor.author Amiza Amir
dc.contributor.author Naimah Yaakob
dc.contributor.author S Nazemi
dc.date.accessioned 2025-10-27T03:20:47Z
dc.date.available 2025-10-27T03:20:47Z
dc.date.issued 2020
dc.description.abstract The manufacturing of a printed circuit board in the SMT assembly line goes through multiple phases of automatic handling. To ensure the quality of the board and reduce the number of defects, inspection tasks such as solder paste inspection and automatic optical inspection are conducted. The inspection tasks are carried out at various phases of the assembly line. The paper aims to answer the questions of how machine learning technology can contribute for better PCB fault detection in the assembly line and at which parts of the assembly line this technology has been applied. The paper discusses the PCB defect detection by using machine learning and other approaches. The current research shows that PCB defect detection using machine learning are miniscule. Early detection is still unexplored and experimented in the industry.
dc.identifier.doi 10.1088/1757-899X/767/1/012064
dc.identifier.uri https://iopscience.iop.org/article/10.1088/1757-899X/767/1/012064/pdf
dc.identifier.uri https://iopscience.iop.org/
dc.identifier.uri https://hdl.handle.net/20.500.14170/14918
dc.language.iso en
dc.publisher IOP Publishing
dc.relation.conference 1st International Symposium on Engineering and Technology (ISETech) 2019, Perlis, Malaysia
dc.relation.ispartof IOP Conference Series
dc.relation.ispartofseries Materials Science and Engineering
dc.relation.issn 1757-8981
dc.relation.issn 1757-899X
dc.title Automated detection of Printed Circuit Boards (PCB) defects by using machine learning in electronic manufacturing: current approaches
dc.type Resource Types::text::conference output::conference proceedings
dspace.entity.type Publication
oaire.citation.endPage 8
oaire.citation.issue 1
oaire.citation.startPage 1
oaire.citation.volume 767
oairecerif.author.affiliation Universiti Malaysia Perlis
oairecerif.author.affiliation Universiti Malaysia Perlis
oairecerif.author.affiliation Universiti Malaysia Perlis
oairecerif.author.affiliation Universiti Malaysia Perlis
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