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DC Field | Value | Language |
---|---|---|
dc.citation.endPage | 212 | - |
dc.citation.startPage | 202 | - |
dc.citation.title | RELIABILITY ENGINEERING & SYSTEM SAFETY | - |
dc.citation.volume | 184 | - |
dc.contributor.author | Oh, YeongGwang | - |
dc.contributor.author | Ransikarbum, Kasin | - |
dc.contributor.author | Busogi, Moise | - |
dc.contributor.author | Kwon, Daeil | - |
dc.contributor.author | Kim, Namhun | - |
dc.date.accessioned | 2023-12-21T19:17:01Z | - |
dc.date.available | 2023-12-21T19:17:01Z | - |
dc.date.created | 2018-03-20 | - |
dc.date.issued | 2019-04 | - |
dc.description.abstract | Quality assessment in many production processes typically relies on manual inspections due to a lack of reference data and an effective method to classify defects in a systematic way. Recently, the real-time, automated approach for product quality assessment has been regarded an important aspect for smart manufacturing applications, such as in the automotive industry. In this research, we suggest a framework to pre-process the data for SVM-based decision making and implement the algorithm in the self-evolving quality assessment system based on the adaptive support vector machine (ASVM) model. An adaptive process is a feedback control that ensures the effectiveness of the support vector machine (SVM) algorithm over time and enables the improvement of SVM-based quality assessment in the real production process. Next, an industrial case study of a primer-sealer dispensing process in a sunroof assembly line of an automobile is illustrated with statistical analysis to verify and validate the applicability and effectiveness of the proposed ASVM-based quality assessment system. Defective patterns are then analyzed using an infrared thermal image of primer-sealer dispensing in a manufacturing process, which contains multi-modal data of dimensional information and temperature deviation from the dispending patterns in our study. | - |
dc.identifier.bibliographicCitation | RELIABILITY ENGINEERING & SYSTEM SAFETY, v.184, pp.202 - 212 | - |
dc.identifier.doi | 10.1016/j.ress.2018.03.020 | - |
dc.identifier.issn | 0951-8320 | - |
dc.identifier.scopusid | 2-s2.0-85044298215 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/23973 | - |
dc.identifier.url | https://www.sciencedirect.com/science/article/pii/S0951832017303861 | - |
dc.identifier.wosid | 000458590200019 | - |
dc.language | 영어 | - |
dc.publisher | ELSEVIER SCI LTD | - |
dc.title | Adaptive SVM-based Real-time Quality Assessment for Primer-Sealer Dispensing Process of Sunroof Assembly Line | - |
dc.type | Article | - |
dc.description.isOpenAccess | FALSE | - |
dc.relation.journalWebOfScienceCategory | Engineering, Industrial; Operations Research & Management Science | - |
dc.relation.journalResearchArea | Engineering; Operations Research & Management Science | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | Quality assessment system | - |
dc.subject.keywordAuthor | Infrared thermography (IRT) | - |
dc.subject.keywordAuthor | Support vector machine (SVM) | - |
dc.subject.keywordAuthor | Machine learning | - |
dc.subject.keywordAuthor | Automotive industry | - |
dc.subject.keywordPlus | SUPPORT VECTOR MACHINES | - |
dc.subject.keywordPlus | DEFECT IDENTIFICATION | - |
dc.subject.keywordPlus | FAULT-DIAGNOSIS | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.subject.keywordPlus | THERMOGRAPHY | - |
dc.subject.keywordPlus | PERFORMANCE | - |
dc.subject.keywordPlus | ALGORITHMS | - |
dc.subject.keywordPlus | PREDICTION | - |
dc.subject.keywordPlus | SYSTEM | - |
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