Full metadata record
DC Field | Value | Language |
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dc.citation.endPage | 132467 | - |
dc.citation.startPage | 132455 | - |
dc.citation.title | IEEE ACCESS | - |
dc.citation.volume | 9 | - |
dc.contributor.author | Kim, Gyeongho | - |
dc.contributor.author | Choi, Jae Gyeong | - |
dc.contributor.author | Ku, Minjoo | - |
dc.contributor.author | Cho, Hyewon | - |
dc.contributor.author | Lim, Sunghoon | - |
dc.date.accessioned | 2023-12-21T15:15:57Z | - |
dc.date.available | 2023-12-21T15:15:57Z | - |
dc.date.created | 2021-10-05 | - |
dc.date.issued | 2021-09 | - |
dc.description.abstract | The authors of this work propose a deep learning-based fault detection model that can be implemented in the field of plastic injection molding. Compared to conventional approaches to fault detection in this domain, recent deep learning approaches prove useful for on-site problems involving complex underlying dynamics with a large number of variables. In addition, the advent of advanced sensors that generate data types in multiple modalities prompts the need for multimodal learning with deep neural networks to detect faults. This process is able to facilitate information from various modalities in an end-to-end learning fashion. The proposed deep learning-based approach opts for an early fusion scheme, in which the low-level feature representations of modalities are combined. A case study involving real-world data, obtained from a car parts company and related to a car window side molding process, validates that the proposed model outperforms late fusion methods and conventional models in solving the problem. | - |
dc.identifier.bibliographicCitation | IEEE ACCESS, v.9, pp.132455 - 132467 | - |
dc.identifier.doi | 10.1109/access.2021.3115665 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.scopusid | 2-s2.0-85115809927 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/54063 | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/9548039 | - |
dc.identifier.wosid | 000702544000001 | - |
dc.language | 영어 | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | A Multimodal Deep Learning-Based Fault Detection Model for a Plastic Injection Molding Process | - |
dc.type | Article | - |
dc.description.isOpenAccess | TRUE | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information SystemsEngineering, Electrical & ElectronicTelecommunications | - |
dc.relation.journalResearchArea | Computer ScienceEngineeringTelecommunications | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | Fault detectionInjection moldingPlasticsData modelsDeep learningFeature extractionTemperature measurementMachine learningdeep learningmultimodal learningearly fusionindustrial AIplastic injection molding | - |
dc.subject.keywordPlus | NEAREST-NEIGHBOR RULECONVOLUTIONAL NEURAL-NETWORKSEMOTION RECOGNITIONROTATING MACHINERYDIAGNOSISCLASSIFICATIONPREDICTIONQUALITYSYSTEMREAL | - |
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