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Optimal variable screening in automotive crankshaft remanufacturing process using statistical pattern recognition approach in the Mahalanobis-Taguchi system

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dc.citedby1
dc.contributor.authorMuhamad W.Z.A.W.en_US
dc.contributor.authorJamaludin K.R.en_US
dc.contributor.authorMuhtazaruddin M.N.en_US
dc.contributor.authorYahya Z.R.en_US
dc.contributor.authorRamlie F.en_US
dc.contributor.authorHarudin N.en_US
dc.contributor.authorid55860800560en_US
dc.contributor.authorid26434395500en_US
dc.contributor.authorid55578437800en_US
dc.contributor.authorid50862369800en_US
dc.contributor.authorid55982859700en_US
dc.contributor.authorid56319654100en_US
dc.date.accessioned2023-05-29T06:50:52Z
dc.date.available2023-05-29T06:50:52Z
dc.date.issued2018
dc.description.abstractThe Mahalanobis-Taguchi System (MTS) refers to a newly-developed technique based on statistics that integrates a number of mathematical concepts to be applied for classification and diagnosis purposes within systems that are comprised of multiple dimensions. The MTS has been proven to be an exceptional technique that can be employed in numerous fields. In MTS, it is essential to choose the variables in order to enhance the accuracy in classifying via orthogonal array (OA) and Signal-to-Noise (S/N) ratios. However, the penalty for over-fitting or regularization is not included in the feature selection process for the MTS classifier. Hence, this paper investigated the combination between MTS and statistical pattern recognition approach applied to automotive crankshaft remanufacturing as an automated decision-making tool for classification purposes. The outcomes revealed that MTS displayed better classification performance for both training and test datasets, besides eliminating redundant and irrelevant parameters better than the conventional approach did. � 2018 Author(s).en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo20031
dc.identifier.doi10.1063/1.5054230
dc.identifier.scopus2-s2.0-85054590137
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85054590137&doi=10.1063%2f1.5054230&partnerID=40&md5=70a32b6078e1e1f059d522081576bce5
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23665
dc.identifier.volume2013
dc.publisherAmerican Institute of Physics Inc.en_US
dc.sourceScopus
dc.sourcetitleAIP Conference Proceedings
dc.titleOptimal variable screening in automotive crankshaft remanufacturing process using statistical pattern recognition approach in the Mahalanobis-Taguchi systemen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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