Utilize este identificador para referenciar este registo: https://hdl.handle.net/1822/70508

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Campo DCValorIdioma
dc.contributor.authorDelgado, Pedropor
dc.contributor.authorMartins, Cristinapor
dc.contributor.authorBraga, A. C.por
dc.contributor.authorBarros, Cláudiapor
dc.contributor.authorDelgado, Isabelpor
dc.contributor.authorMarques, Carlospor
dc.contributor.authorSampaio, Paulopor
dc.date.accessioned2021-03-01T16:25:08Z-
dc.date.available2021-03-01T16:25:08Z-
dc.date.issued2018-
dc.identifier.citationDelgado P. et al. (2018) Benefits of Multivariate Statistical Process Control Based on Principal Component Analysis in Solder Paste Printing Process Where 100% Automatic Inspection Is Already Installed. In: Gervasi O. et al. (eds) Computational Science and Its Applications – ICCSA 2018. ICCSA 2018. Lecture Notes in Computer Science, vol 10961. Springer, Cham. https://doi.org/10.1007/978-3-319-95165-2_25-
dc.identifier.isbn978-3-319-95164-5-
dc.identifier.issn0302-9743-
dc.identifier.urihttps://hdl.handle.net/1822/70508-
dc.description.abstractThe process of printing and inspecting solder paste deposits in Printed Circuit Boards (PCB) involves a very large number of variables (more than 30000 can be found in 3D inspection of high density PCBs). State of the art Surface Mount Technology (SMT) production lines rely on 100% inspection of all paste deposits for each PCB produced. Specification limits for Area, Height, Volume, Offset X and Offset Y have been defined based on detailed and consolidated studies. PCBs with paste deposits failing the defined criteria, are proposed to be rejected. The study of the variation of the rejected fraction over time, has shown that the process is not always stable and it would benefit from a statistical process control approach. Statistical process control for 30000 variables is not feasible with a univariate approach. On one side, it is not possible to pay attention to such a high number of Shewhart control charts. On the other side, the very rich information contained in the evolution of the correlation structure would be lost in the case of a univariate approach. The use of Multivariate Statistical Process Control based on Principal Component Analysis (PCA-MSPC) provides an efficient solution for this problem. The examples discussed in this paper show that PCA-MSPC in solder paste printing is able to detect and diagnose disturbances in the underlying factors which govern the variation of the process. The early identification of these disturbances can be used to trigger corrective actions before disturbances start to cause defects. The immediate confirmation of effectiveness of the corrective action is a characteristic offered by this method and can be observed in all the examples presented.por
dc.description.sponsorshipINCT-EN - Instituto Nacional de Ciência e Tecnologia para Excitotoxicidade e Neuroproteção(UID/CEC/00319/2013). European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) Project nº 002814; Funding Reference: POCI-01-0247-FEDER-002814por
dc.language.isoengpor
dc.publisherSpringerpor
dc.relationPOCI-01-0247-FEDER-002814por
dc.relationinfo:eu-repo/grantAgreement/FCT/5876/147280/PTpor
dc.rightsopenAccesspor
dc.subjectHotelling’s T 2por
dc.subjectMultivariate statistical process controlpor
dc.subjectNormal operation conditionspor
dc.subjectPrincipal component analysispor
dc.subjectSolder Paste Inspectionpor
dc.subjectSquared prediction errorpor
dc.subjectVariable contributionspor
dc.subjectMultivariate Statistical Process Control Principal Component Analysispor
dc.titleBenefits of multivariate statistical process control based on principal component analysis in solder paste printing process where 100% automatic inspection is already installedpor
dc.typeconferencePaperpor
dc.peerreviewedyespor
dc.relation.publisherversionhttps://link.springer.com/chapter/10.1007/978-3-319-95165-2_25por
oaire.citationStartPage351por
oaire.citationEndPage365por
oaire.citationVolume10961 LNCSpor
dc.date.updated2021-03-01T15:50:41Z-
dc.identifier.doi10.1007/978-3-319-95165-2_25por
dc.subject.wosScience & Technology-
sdum.export.identifier8982-
sdum.journalLecture Notes in Computer Sciencepor
sdum.conferencePublicationInternational Conference on Computational Science and Its Applicationspor
sdum.bookTitleComputational Science and Its Applications – ICCSA 2018por
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