Please use this identifier to cite or link to this item: https://hdl.handle.net/1822/78214

TitleScrewing process monitoring using MSPC in large scale smart manufacturing
Author(s)Teixeira, Humberto Nuno
Lopes, Isabel da Silva
Braga, A. C.
Delgado, Pedro
Martins, Cristina
KeywordsMultivariate Statistical Process Control (MSPC)
Principal Component Analysis (PCA)
Screwing process
Smart manufacturing
Issue date2022
PublisherSpringer
JournalLecture Notes in Mechanical Engineering
CitationTeixeira, H.N., Lopes, I., Braga, A.C., Delgado, P., Martins, C. (2022). Screwing Process Monitoring Using MSPC in Large Scale Smart Manufacturing. In: Machado, J., Soares, F., Trojanowska, J., Yildirim, S. (eds) Innovations in Mechatronics Engineering. icieng 2021. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-79168-1_1
Abstract(s)The ability to obtain useful information to support decision-making from big data sets delivered by sensors can significantly contribute to enhance smart manufacturing initiatives. This paper presents the results of a study performed in an automotive electronics assembly line. An approach that uses Multivariate Statistical Process Control based on Principal Component Analysis (MSPC-PCA) was applied to early detect undesirable changes in the screwing processes performance by extracting relevant information from the torque-angle curve data. Since the data of different torque-angle curves are not aligned, the proposed approach includes the linear interpolation of the original data to enable Principal Component Analysis (PCA). PCA proved to be an appropriate technique to obtain significant information from the process variables, which consist of the successive value of the torque at constant angular intervals. Score plots and multivariate control charts were used to detect defective tightening and identify behaviors that represent inefficient tightening. This is a new approach that can be applied to effectively monitor screwing processes in the assembly of different products either periodically or in real-time.
TypeConference paper
URIhttps://hdl.handle.net/1822/78214
ISBN978-3-030-79167-4
DOI10.1007/978-3-030-79168-1_1
ISSN2195-4356
Publisher versionhttps://link.springer.com/chapter/10.1007/978-3-030-79168-1_1
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings

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