Academic Journal
View in EDS

ATTACHABLE IOT-BASED DIGITAL TWIN FRAMEWORK SPECIALIZED FOR SME PRODUCTION LINES.

Bibliographic Details
Title: ATTACHABLE IOT-BASED DIGITAL TWIN FRAMEWORK SPECIALIZED FOR SME PRODUCTION LINES.
Authors: Kang, B. G.1 bgkang@kitech.re.kr, Kim, B. S.2 bskim@seoultech.ac.kr
Source: International Journal of Simulation Modelling (IJSIMM). Sep2024, Vol. 23 Issue 3, p471-482. 12p.
Subject Terms: *MACHINE learning, *DIGITAL twin, *DIGITAL transformation, *SMALL business, *ECONOMIC uncertainty
Abstract: While large enterprises are actively preparing for digital transformation by leveraging technologies such as digital twins, smaller companies face challenges due to economic constraints and market uncertainties, leading to a relative lack of awareness and readiness. To address this situation, this study proposes a digital twin development framework tailored for small and medium-sized enterprises (SMEs). This framework utilizes attachable IoT devices for real-time collection of manufacturing data and leverages public server systems for data management. Moreover, it enables the refinement and optimization of digital twins by training machine learning models on collected data. Additionally, the framework includes the integration of simulation models and machine learning models for comprehensive digital twin modelling. Finally, the paper suggests a process for applying and validating this framework in real manufacturing companies, demonstrating the effects of digital twin implementation on productivity enhancement in the production lines of two SMEs. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Simulation Modelling (IJSIMM) is the property of DAAAM International and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Academic Search Complete
FullText Links:
  – Type: pdflink
Text:
  Availability: 0
CustomLinks:
  – Url: https://resolver.ebsco.com/c/xy5jbn/result?sid=EBSCO:a9h&genre=article&issn=17264529&ISBN=&volume=23&issue=3&date=20240901&spage=471&pages=471-482&title=International Journal of Simulation Modelling (IJSIMM)&atitle=ATTACHABLE%20IOT-BASED%20DIGITAL%20TWIN%20FRAMEWORK%20SPECIALIZED%20FOR%20SME%20PRODUCTION%20LINES.&aulast=Kang%2C%20B.%20G.&id=DOI:10.2507/IJSIMM23-3-694
    Name: Full Text Finder (for New FTF UI) (s8985755)
    Category: fullText
    Text: Find It @ SCU Libraries
    MouseOverText: Find It @ SCU Libraries
Header DbId: a9h
DbLabel: Academic Search Complete
An: 179405021
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: ATTACHABLE IOT-BASED DIGITAL TWIN FRAMEWORK SPECIALIZED FOR SME PRODUCTION LINES.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Kang%2C+B%2E+G%2E%22">Kang, B. G.</searchLink><relatesTo>1</relatesTo><i> bgkang@kitech.re.kr</i><br /><searchLink fieldCode="AR" term="%22Kim%2C+B%2E+S%2E%22">Kim, B. S.</searchLink><relatesTo>2</relatesTo><i> bskim@seoultech.ac.kr</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Simulation+Modelling+%28IJSIMM%29%22">International Journal of Simulation Modelling (IJSIMM)</searchLink>. Sep2024, Vol. 23 Issue 3, p471-482. 12p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22MACHINE+learning%22">MACHINE learning</searchLink><br />*<searchLink fieldCode="DE" term="%22DIGITAL+twin%22">DIGITAL twin</searchLink><br />*<searchLink fieldCode="DE" term="%22DIGITAL+transformation%22">DIGITAL transformation</searchLink><br />*<searchLink fieldCode="DE" term="%22SMALL+business%22">SMALL business</searchLink><br />*<searchLink fieldCode="DE" term="%22ECONOMIC+uncertainty%22">ECONOMIC uncertainty</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: While large enterprises are actively preparing for digital transformation by leveraging technologies such as digital twins, smaller companies face challenges due to economic constraints and market uncertainties, leading to a relative lack of awareness and readiness. To address this situation, this study proposes a digital twin development framework tailored for small and medium-sized enterprises (SMEs). This framework utilizes attachable IoT devices for real-time collection of manufacturing data and leverages public server systems for data management. Moreover, it enables the refinement and optimization of digital twins by training machine learning models on collected data. Additionally, the framework includes the integration of simulation models and machine learning models for comprehensive digital twin modelling. Finally, the paper suggests a process for applying and validating this framework in real manufacturing companies, demonstrating the effects of digital twin implementation on productivity enhancement in the production lines of two SMEs. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Simulation Modelling (IJSIMM) is the property of DAAAM International and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://login.libproxy.scu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&scope=site&db=a9h&AN=179405021
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.2507/IJSIMM23-3-694
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 12
        StartPage: 471
    Subjects:
      – SubjectFull: MACHINE learning
        Type: general
      – SubjectFull: DIGITAL twin
        Type: general
      – SubjectFull: DIGITAL transformation
        Type: general
      – SubjectFull: SMALL business
        Type: general
      – SubjectFull: ECONOMIC uncertainty
        Type: general
    Titles:
      – TitleFull: ATTACHABLE IOT-BASED DIGITAL TWIN FRAMEWORK SPECIALIZED FOR SME PRODUCTION LINES.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Kang, B. G.
      – PersonEntity:
          Name:
            NameFull: Kim, B. S.
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 09
              Text: Sep2024
              Type: published
              Y: 2024
          Identifiers:
            – Type: issn-print
              Value: 17264529
          Numbering:
            – Type: volume
              Value: 23
            – Type: issue
              Value: 3
          Titles:
            – TitleFull: International Journal of Simulation Modelling (IJSIMM)
              Type: main
ResultId 1