Migration from the traditional to the smart factory in the die-casting industry: Novel process data acquisition and fault detection based on artificial neural network
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Jeongsu | - |
dc.contributor.author | Lee, Young Chul | - |
dc.contributor.author | Kim, Jeong Tae | - |
dc.date.accessioned | 2022-04-05T00:40:06Z | - |
dc.date.available | 2022-04-05T00:40:06Z | - |
dc.date.created | 2022-04-05 | - |
dc.date.issued | 2021-04 | - |
dc.identifier.issn | 0924-0136 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/83897 | - |
dc.description.abstract | Although die-casting is one of the most popular mass production processes of precise metal parts, the manufacturing environment of the die-casting factory remains at the traditional level. In this study, we developed three core technologies to realize a smart-factory platform for die-casting industry: 1) a novel cost-effective product-tracking technology to obtain high-quality process data providing individual product information, 2) an advanced process data acquisition system that considers process failure, and 3) a fault detection module based on an artificial neural network. Our newly developed systems for the die-casting process were verified using 1500 test production. Based on the pilot production data, we developed a fault detection module with the pre-processing of time series temperature and pressure measurement data. The developed fault detection module shows 96.9 % accuracy for untrained data. The technologies developed in this study are expected to be a promising smart-factory platform to reduce the defect rate and production cost in die-casting industry. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER SCIENCE SA | - |
dc.relation.isPartOf | JOURNAL OF MATERIALS PROCESSING TECHNOLOGY | - |
dc.title | Migration from the traditional to the smart factory in the die-casting industry: Novel process data acquisition and fault detection based on artificial neural network | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000606784000008 | - |
dc.identifier.doi | 10.1016/j.jmatprotec.2020.116972 | - |
dc.identifier.bibliographicCitation | JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, v.290 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85096852411 | - |
dc.citation.title | JOURNAL OF MATERIALS PROCESSING TECHNOLOGY | - |
dc.citation.volume | 290 | - |
dc.contributor.affiliatedAuthor | Lee, Jeongsu | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Die-casting | - |
dc.subject.keywordAuthor | Fault detection | - |
dc.subject.keywordAuthor | Smart factory | - |
dc.subject.keywordAuthor | Industrial data acquisition | - |
dc.subject.keywordAuthor | Artificial neural network | - |
dc.subject.keywordPlus | PROCESS PARAMETERS | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Materials Science | - |
dc.relation.journalWebOfScienceCategory | Engineering, Industrial | - |
dc.relation.journalWebOfScienceCategory | Engineering, Manufacturing | - |
dc.relation.journalWebOfScienceCategory | Materials Science, Multidisciplinary | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea(13120)031-750-5114
COPYRIGHT 2020 Gachon University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.