Publicação
A deep regression model with low-dimensional feature extraction for multi-parameter manufacturing quality prediction
| datacite.subject.sdg | 09:Indústria, Inovação e Infraestruturas | |
| datacite.subject.sdg | 12:Produção e Consumo Sustentáveis | |
| dc.contributor.author | Deng, Jun | |
| dc.contributor.author | Bai, Yun | |
| dc.contributor.author | Li, Chuan | |
| dc.date.accessioned | 2026-02-24T12:14:44Z | |
| dc.date.available | 2026-02-24T12:14:44Z | |
| dc.date.issued | 2020-04-06 | |
| dc.description.abstract | Manufacturing quality prediction can be used to design better parameters at an earlier production stage. However, in complex manufacturing processes, prediction performance is a_ected by multi-parameter inputs. To address this issue, a deep regression framework based on manifold learning (MDRN) is proposed in this paper. The multi-parameter inputs (i.e., high-dimensional information) were firstly analyzed using manifold learning (ML), which is an e_ective nonlinear technique for low-dimensional feature extraction that can enhance the representation of multi-parameter inputs and reduce calculation burdens. The features obtained through the ML were then learned by a deep learning architecture (DL). It can learn su_cient features of the pattern between manufacturing quality and the low-dimensional information in an unsupervised framework, which has been proven to be e_ective in many fields. Finally, the learned features were inputted into the regression network, and manufacturing quality predictions were made. One type (two cases) of machinery parts manufacturing system was investigated in order to estimate the performance of the proposed MDRN with three comparisons. The experiments showed that the MDRN overwhelmed all the peer methods in terms of mean absolute percentage error, root-mean-square error, and threshold statistics. Based on these results, we conclude that integrating the ML technique for dimension reduction and the DL technique for feature extraction can improve multi-parameter manufacturing quality predictions. | eng |
| dc.description.sponsorship | 71801044; 51775112; 201908500020; 2019B1515120095; KCYKYQD2017011 | |
| dc.identifier.doi | 10.3390/app10072522 | |
| dc.identifier.issn | 2076-3417 | |
| dc.identifier.uri | http://hdl.handle.net/10400.1/28237 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | MDPI | |
| dc.relation.ispartof | Applied Sciences | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Deep regression network | |
| dc.subject | Multi-parameter | |
| dc.subject | Low-dimensional feature | |
| dc.subject | Manufacturing quality | |
| dc.subject | Prediction | |
| dc.title | A deep regression model with low-dimensional feature extraction for multi-parameter manufacturing quality prediction | eng |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.issue | 7 | |
| oaire.citation.title | Applied sciences | |
| oaire.citation.volume | 10 | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |
| person.familyName | Bai | |
| person.givenName | Yun | |
| person.identifier.orcid | 0000-0003-2710-7994 | |
| person.identifier.scopus-author-id | 55461096500 | |
| relation.isAuthorOfPublication | 395ae945-8e87-47b3-9edf-6fa1f380097f | |
| relation.isAuthorOfPublication.latestForDiscovery | 395ae945-8e87-47b3-9edf-6fa1f380097f |