Research on Pcb Defect Detection Using Artificial Intelligence: a Systematic Mapping Study

dc.authorscopusid 58966313400
dc.authorscopusid 57271674300
dc.contributor.author Ural, Dogan Irmak
dc.contributor.author Sezen, Arda
dc.contributor.other Computer Engineering
dc.date.accessioned 2024-07-05T15:22:50Z
dc.date.available 2024-07-05T15:22:50Z
dc.date.issued 2024
dc.department Atılım University en_US
dc.department-temp [Ural, Dogan Irmak; Sezen, Arda] Atilim Univ, Dept Comp Engn, TR-06830 Ankara, Turkiye en_US
dc.description.abstract SMT (Surface Mount Technology) has been the backbone of PCB (Printed Circuit Board) production for the last couple of decades. Even though the speed and accuracy of SMT have been drastically improved in the last decade, errors during production are still a very valid problem for the PCB industry. With the exponential rise of Artificial Intelligence in the last decade, the SMT industry was one of the most eager industries to use this new technology to detect possible defects during production. Lately, traditional image processing techniques started to lag behind methods such as machine learning and deep learning when the discussion came to the need of high accuracy. In this paper, we screen academic libraries to understand which of the latest methods and techniques are used in the domain and to deduce a general process for detecting defects in PCBs. During the research we have investigated research questions related to state-of-the-art methods, highly mentioned datasets, and sought after PCB defects. All findings and answers are mapped to be able to understand where this pursuit might point towards. From a total of 270 papers, 90 of them were addressed in detail and 78 papers were chosen for this systematic mapping. en_US
dc.identifier.citationcount 0
dc.identifier.doi 10.1007/s12065-024-00930-x
dc.identifier.issn 1864-5909
dc.identifier.issn 1864-5917
dc.identifier.scopus 2-s2.0-85189201281
dc.identifier.scopusquality Q2
dc.identifier.uri https://doi.org/10.1007/s12065-024-00930-x
dc.identifier.uri https://hdl.handle.net/20.500.14411/2244
dc.identifier.wos WOS:001195694000001
dc.institutionauthor Sezen, Arda
dc.language.iso en en_US
dc.publisher Springer Heidelberg en_US
dc.relation.publicationcategory Diğer en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 2
dc.subject Artificial intelligence en_US
dc.subject Image processing en_US
dc.subject Defect detection en_US
dc.subject PCB en_US
dc.subject Systematic mapping en_US
dc.title Research on Pcb Defect Detection Using Artificial Intelligence: a Systematic Mapping Study en_US
dc.type Review en_US
dc.wos.citedbyCount 2
dspace.entity.type Publication
relation.isAuthorOfPublication 367853fe-83ca-445e-a3be-00c62fcb4e35
relation.isAuthorOfPublication.latestForDiscovery 367853fe-83ca-445e-a3be-00c62fcb4e35
relation.isOrgUnitOfPublication e0809e2c-77a7-4f04-9cb0-4bccec9395fa
relation.isOrgUnitOfPublication.latestForDiscovery e0809e2c-77a7-4f04-9cb0-4bccec9395fa

Files

Collections