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A manufacturing quality prediction model based on AdaBoost-LSTM with rough knowledge

2021-05Journal article Restricted access
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dc.contributor.authorBai, Yun
dc.contributor.authorXie, Jingjing
dc.contributor.authorWang, Dongqiang
dc.contributor.authorZhang, Wanjuan
dc.contributor.authorLi, Chuan
dc.date.accessioned2021-09-08T10:57:55Z
dc.date.available2021-09-08T10:57:55Z
dc.date.issued2021-05
dc.description.abstractManufacturing quality prediction is one of the significant concerns in modern enterprise production management, which provides data support for reliability assessment and parameter optimization, thus improving the intelligent management level of enterprises and helping achieve high-quality products at lower costs. In this paper, an ensemble learning framework using rough knowledge is proposed for manufacturing quality prediction. The proposed model consists of three elements: (1) significant parameters in different production stages are selected based on attribute reduction and decision rule extraction of rough set theory (RS), (2) long short-term memory network (LSTM) is utilized for building the relationship between the significant parameters and manufacturing quality, and (3) the learning performance of the LSTM is reinforced by AdaBoost approach. To estimate the effectiveness of the proposed model, a competition dataset about manufacturing quality control is applied and six models are investigated. The comparison experiments show that the proposed model overwhelms all the comparison models in terms of root-mean-square error, threshold statistics and residuals analysis. In addition, the proposed model has statistically significant difference from all the comparative models. It is recommended from this work that the ensemble learning technique integrating the rough knowledge synchronously improves the sensitivity and regression capacity of the model.
dc.description.sponsorshipNational Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [71801044]; Natural Science Foundation of ChongqingNatural Science Foundation of Chongqing [cstc2018jcyjAX0436, cstc2019jcyj-zdxmX0013, cstc2019jscx-fxydX0077]; Project of China Scholarship Council [201908500020]; Open Grant of Chongqing Technology and Business University [1756012, KFJJ2018106]
dc.identifier.doi10.1016/j.cie.2021.107227
dc.identifier.issn0360-8352
dc.identifier.urihttp://hdl.handle.net/10400.1/16975
dc.language.isoeng
dc.peerreviewedyes
dc.publisherPERGAMON-ELSEVIER SCIENCE LTD
dc.subjectManufacturing quality
dc.subjectPrediction
dc.subjectRough set
dc.subjectLong short-term memory
dc.subjectAdaBoost ensemble learning
dc.subject.otherComputer Science; Engineering
dc.titleA manufacturing quality prediction model based on AdaBoost-LSTM with rough knowledge
dc.typejournal article
dspace.entity.typePublication
oaire.citation.startPage107227
oaire.citation.titleComputers & Industrial Engineering
oaire.citation.volume155
person.familyNameBai
person.givenNameYun
person.identifier.orcid0000-0003-2710-7994
person.identifier.scopus-author-id55461096500
rcaap.rightsrestrictedAccess
rcaap.typearticle
relation.isAuthorOfPublication395ae945-8e87-47b3-9edf-6fa1f380097f
relation.isAuthorOfPublication.latestForDiscovery395ae945-8e87-47b3-9edf-6fa1f380097f

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