Repository logo
 
Publication

A comparison of dimension reduction techniques for support vector machine modeling of multi-parameter manufacturing quality prediction

2019-06Journal article Restricted access
Simple item page
dc.contributor.authorBai, Yun
dc.contributor.authorSun, Zhenzhong
dc.contributor.authorZeng, Bo
dc.contributor.authorLong, Jianyu
dc.contributor.authorLi, Lin
dc.contributor.authorValente de Oliveira, JOSÉ
dc.contributor.authorLi, Chuan
dc.date.accessioned2020-07-24T10:52:14Z
dc.date.available2020-07-24T10:52:14Z
dc.date.issued2019-06
dc.description.abstractManufacturing quality prediction model, as an effective measure to monitor the quality in advance, has been developed using various data-driven techniques. However, multi-parameter in multi-stage of the modern manufacturing industry brings about the curse of dimensionality, leading to the difficulties for feature extraction, learning and quality modeling. To address this issue, three dimension reduction techniques are investigated in this paper, i.e., principal component analysis (PCA), locally linear embedding (LLE), and isometric mapping (Isomap). Specifically, the PCA is a linear dimension reduction technique, the LLE is a nonlinear reduction technique with local perspective, and the Isomap is a nonlinear reduction technique from global perspective. After getting the low-dimensional information from the PCA, the LLE, and the Isomap methods respectively, a support vector machine (SVM) is utilized for modeling. To reveal the effectiveness of the dimension reduction techniques and compare the difference of the three dimension reduction techniques, two experimental manufacturing data are collected from a competition about manufacturing quality control in Tianchi Data Lab of China. The comparison experiments indicate that the dimension reduction techniques have capacity for improving the SVM modeling performance indeed, and the Isomap-SVM model with the nonlinear global dimension reduction outperforms all the candidate models in terms of qualitative and quantitative analysis.
dc.description.sponsorshipNational Natural Science Foundation of ChinaNational Natural Science Foundation of China [51775112, 71771033]
dc.description.sponsorshipPostdoctoral Science Foundation of ChinaChina Postdoctoral Science Foundation [2016M602459]
dc.description.sponsorshipResearch Program of Higher Education of Guangdong [2016KZDXM054]
dc.description.versioninfo:eu-repo/semantics/publishedVersion
dc.identifier.doi10.1007/s10845-017-1388-1
dc.identifier.issn0956-5515
dc.identifier.issn1572-8145
dc.identifier.urihttp://hdl.handle.net/10400.1/14336
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer
dc.subjectRegression
dc.subjectSystem
dc.titleA comparison of dimension reduction techniques for support vector machine modeling of multi-parameter manufacturing quality prediction
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage2256
oaire.citation.issue5
oaire.citation.startPage2245
oaire.citation.titleJournal of Intelligent Manufacturing
oaire.citation.volume30
person.familyNameBai
person.familyNameLUÍS VALENTE DE OLIVEIRA
person.givenNameYun
person.givenNameJOSÉ
person.identifier.ciencia-id1F12-C1D3-7717
person.identifier.orcid0000-0003-2710-7994
person.identifier.orcid0000-0001-5337-5699
person.identifier.scopus-author-id55461096500
rcaap.rightsrestrictedAccess
rcaap.typearticle
relation.isAuthorOfPublication395ae945-8e87-47b3-9edf-6fa1f380097f
relation.isAuthorOfPublicationbb726e73-690c-4a33-822e-c47bdac3035b
relation.isAuthorOfPublication.latestForDiscoverybb726e73-690c-4a33-822e-c47bdac3035b

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
Bai2019_Article_AComparisonOfDimensionReductio.pdf
Size:
997.29 KB
Format:
Adobe Portable Document Format
Download

Collections

FCT2-Artigos (em revistas ou actas indexadas)