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dc.contributor.authorTeixeira, Humberto Nunopor
dc.contributor.authorLopes, Isabel da Silvapor
dc.contributor.authorBraga, A. C.por
dc.contributor.authorDelgado, Pedropor
dc.contributor.authorMartins, Cristinapor
dc.description.abstractScrews are widely used for parts joining in industry. The definition of effective monitoring strategies for screwing processes can help to prevent or significantly reduce ineffective procedures, defective screwing and downtime. Monitoring several correlated variables simultaneously in order to detect relevant changes in manufacturing processes is an increasingly frequent practice furthered by advanced data acquisition systems. However, the monitoring approaches currently used do not consider the multivariate nature of the screwing processes. This paper presents the results of a study performed in an automotive electronics assembly line. Screwing process data concerning torque and rotation angle were analyzed using multivariate statistical process control based on principal component analysis (MSPC-PCA). The main purpose was to extract relevant information from a high number of correlated variables in order to early detect undesirable changes in the process performance. A PCA model was defined based on three principal components. The physical meaning of each component was identified, and underlying causes were inferred based on technical knowledge about the process. Monitoring tools, such as score plots and multivariate control charts allowed to detect the defective screwing cases included in the analyzed data set. Furthermore, eight periods of instability were identified. Considering that the out-of-control signals detected in these periods mainly correspond to delays at the beginning of the tightening operation, four potential causes to explain this behavior were ascertained and analyzed. This research allowed to acquire a deeper understanding on the screwing process behavior and about the causes with higher impact on its stability. Due to its flexibility and versatility, it is considered that this approach can be applied to effectively monitor screwing ppor
dc.description.sponsorshipCEC - Clinical Excellence Commission(undefined)por
dc.publisherElsevier B.V.por
dc.subjectMultivariate statistical process control (MSPC)por
dc.subjectPrincipal component analysis (PCA)por
dc.subjectScrewing processpor
dc.titleScrewing process analysis using multivariate statistical process controlpor
sdum.journalProcedia Manufacturingpor
sdum.conferencePublicationProcedia Manufacturingpor
Appears in Collections:CAlg - Artigos em revistas internacionais / Papers in international journals

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