Please use this identifier to cite or link to this item: https://hdl.handle.net/1822/70508

TitleBenefits of multivariate statistical process control based on principal component analysis in solder paste printing process where 100% automatic inspection is already installed
Author(s)Delgado, Pedro
Martins, Cristina
Braga, A. C.
Barros, Cláudia
Delgado, Isabel
Marques, Carlos
Sampaio, Paulo
KeywordsHotelling’s T 2
Multivariate statistical process control
Normal operation conditions
Principal component analysis
Solder Paste Inspection
Squared prediction error
Variable contributions
Multivariate Statistical Process Control Principal Component Analysis
Issue date2018
PublisherSpringer
JournalLecture Notes in Computer Science
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
Abstract(s)The 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.
TypeConference paper
URIhttps://hdl.handle.net/1822/70508
ISBN978-3-319-95164-5
DOI10.1007/978-3-319-95165-2_25
ISSN0302-9743
Publisher versionhttps://link.springer.com/chapter/10.1007/978-3-319-95165-2_25
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings

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