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dc.contributor.author김진수-
dc.date.accessioned2022-04-29T07:32:56Z-
dc.date.available2022-04-29T07:32:56Z-
dc.date.issued2021-09-
dc.identifier.citationPOLYMERS; OCT 2021, 13 19, p3297 18p.en_US
dc.identifier.issn20734360-
dc.identifier.urihttps://www.proquest.com/docview/2581014820?accountid=11283-
dc.identifier.urihttps://repository.hanyang.ac.kr/handle/20.500.11754/170433-
dc.description.abstractThe cavity pressure profile representing the effective molding condition in a cavity is closely related to part quality. Analysis of the effect of the cavity pressure profile on quality requires prior knowledge and understanding of the injection-molding process and polymer materials. In this work, an analysis methodology to examine the effect of the cavity pressure profile on part quality is proposed. The methodology uses the interpretation of a neural network as a metamodel representing the relationship between the cavity pressure profile and the part weight as a quality index. The process state points (PSPs) extracted from the cavity pressure profile were used as the input features of the model. The overall impact of the features on the part weight and the contribution of them on a specific sample clarify the influence of the cavity pressure profile on the part weight. The effect of the process parameters on the part weight and the PSPs supported the validity of the methodology. The influential features and impacts analyzed using this methodology can be employed to set the target points and bounds of the monitoring window, and the contribution of each feature can be used to optimize the injection-molding process.en_US
dc.description.sponsorshipThe authors thank Kistler Korea Co., Ltd. Republic of Korea for its support of the process monitoring system.en_US
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.subjectinjection moldingen_US
dc.subjectcavity pressureen_US
dc.subjectinterpretable machine learningen_US
dc.titleNovel Analysis Methodology of Cavity Pressure Profiles in Injection-Molding Processes Using Interpretation of Machine Learning Modelen_US
dc.typeArticleen_US
dc.identifier.doi10.3390/polym13193297-
dc.relation.page3297-3297-
dc.relation.journalPOLYMERS-
dc.contributor.googleauthorGim, Jinsu-
dc.contributor.googleauthorRhee, Byungohk-
dc.relation.code2021002751-
dc.sector.campusE-
dc.sector.daehakRESEARCH INSTITUTE[E]-
dc.sector.departmentRESEARCH INSTITUTE OF ENGINEERING & TECHNOLOGY-
dc.identifier.pidjinsugim-
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RESEARCH INSTITUTE[E](부설연구소) > RESEARCH INSTITUTE OF ENGINEERING & TECHNOLOGY(공학기술연구소) > Articles
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