Virtual factory model development for AI-driven optimization in manufacturing.
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| Title: | Virtual factory model development for AI-driven optimization in manufacturing. |
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| Alternate Title: | Virtuaaltehase mudeli arendamine tehisintellektil põhinevaks tootmise optimeerimiseks. |
| Authors: | Raamets, Tõnis1 tonis.raamets@taltech.ee, Karjust, Kristo1, Hermaste, Aigar1, Kelpman, Karolin1 |
| Source: | Proceedings of the Estonian Academy of Sciences. 2025, Vol. 74 Issue 2, p228-233. 6p. |
| Subjects: | Manufacturing execution systems, Digital twin, Artificial intelligence, Digital computer simulation, Cluster analysis (Statistics) |
| Abstract (English): | This paper examines the development of a virtual factory model to optimize overall equipment effectiveness (OEE) in a planned manufacturing facility. Using digital simulations based on a wood manufacturing setup, AI-driven models can be applied to analyze specific OEE metrics, allowing for targeted identification of production bottlenecks and efficiency improvements. The virtual factory enabled scenario testing for the proposed facility, providing actionable insights without impacting current operations. The preliminary results indicate that AI integration within a virtual factory can significantly enhance planning and decision-making for future production investments. [ABSTRACT FROM AUTHOR] |
| Abstract (Estonian): | Uuringus käsitletakse tehisintellektil põhineva analüüsi rakendamist virtuaaltehase mudeli arendamisel eesmärgiga optimeerida tootmisseadmete üldist tõhusust puidutööstusettevõttes. Uuringus kasutati Siemens Plant Simulationi tarkvara, et luua digitaalne kaksik, mis võimaldab tootmisvoogude simulatsiooni ja analüüsi. Lisaks koguti reaalajas andmeid tootmisjuhtimissüsteemi abil, et mudelit täpsustada ja pakkuda dünaamilist ülevaadet tootmisprotsessidest. Kogutud andmete analüüsimiseks rakendati klastrianalüüsi, mis võimaldas tuvastada kitsaskohti ja ressursikasutuse ebatõhusust. Simulatsioonide ja andmepõhiste soovituste põhjal optimeeriti tööjaamade paigutust ja ressursijaotust, mis parandas tootmisvoogude tasakaalu ja vähendas kitsaskohtade esinemist. Tulemused näitavad, et virtuaaltehase mudelite ja tehisintellekti integreerimine aitab tõsta tootmisvoogude tõhusust, vähendada seisakuid ja suurendada investeeringute planeerimise täpsust. Pakutud lähenemine toetab tänapäevase puidutööstuse vajadust paindlike, skaleeritavate ja kulutõhusate lahenduste järele, järgides Industry 5.0 põhimõtteid. [ABSTRACT FROM AUTHOR] |
| Copyright of Proceedings of the Estonian Academy of Sciences is the property of Teaduste Akadeemia Kirjastus and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. 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: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 185494762 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Virtual factory model development for AI-driven optimization in manufacturing. – Name: TitleAlt Label: Alternate Title Group: TiAlt Data: Virtuaaltehase mudeli arendamine tehisintellektil põhinevaks tootmise optimeerimiseks. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Raamets%2C+Tõnis%22">Raamets, Tõnis</searchLink><relatesTo>1</relatesTo><i> tonis.raamets@taltech.ee</i><br /><searchLink fieldCode="AR" term="%22Karjust%2C+Kristo%22">Karjust, Kristo</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Hermaste%2C+Aigar%22">Hermaste, Aigar</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Kelpman%2C+Karolin%22">Kelpman, Karolin</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Proceedings+of+the+Estonian+Academy+of+Sciences%22">Proceedings of the Estonian Academy of Sciences</searchLink>. 2025, Vol. 74 Issue 2, p228-233. 6p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Manufacturing+execution+systems%22">Manufacturing execution systems</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+twin%22">Digital twin</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+computer+simulation%22">Digital computer simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Cluster+analysis+%28Statistics%29%22">Cluster analysis (Statistics)</searchLink> – Name: Abstract Label: Abstract (English) Group: Ab Data: This paper examines the development of a virtual factory model to optimize overall equipment effectiveness (OEE) in a planned manufacturing facility. Using digital simulations based on a wood manufacturing setup, AI-driven models can be applied to analyze specific OEE metrics, allowing for targeted identification of production bottlenecks and efficiency improvements. The virtual factory enabled scenario testing for the proposed facility, providing actionable insights without impacting current operations. The preliminary results indicate that AI integration within a virtual factory can significantly enhance planning and decision-making for future production investments. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Abstract (Estonian) Group: Ab Data: Uuringus käsitletakse tehisintellektil põhineva analüüsi rakendamist virtuaaltehase mudeli arendamisel eesmärgiga optimeerida tootmisseadmete üldist tõhusust puidutööstusettevõttes. Uuringus kasutati Siemens Plant Simulationi tarkvara, et luua digitaalne kaksik, mis võimaldab tootmisvoogude simulatsiooni ja analüüsi. Lisaks koguti reaalajas andmeid tootmisjuhtimissüsteemi abil, et mudelit täpsustada ja pakkuda dünaamilist ülevaadet tootmisprotsessidest. Kogutud andmete analüüsimiseks rakendati klastrianalüüsi, mis võimaldas tuvastada kitsaskohti ja ressursikasutuse ebatõhusust. Simulatsioonide ja andmepõhiste soovituste põhjal optimeeriti tööjaamade paigutust ja ressursijaotust, mis parandas tootmisvoogude tasakaalu ja vähendas kitsaskohtade esinemist. Tulemused näitavad, et virtuaaltehase mudelite ja tehisintellekti integreerimine aitab tõsta tootmisvoogude tõhusust, vähendada seisakuid ja suurendada investeeringute planeerimise täpsust. Pakutud lähenemine toetab tänapäevase puidutööstuse vajadust paindlike, skaleeritavate ja kulutõhusate lahenduste järele, järgides Industry 5.0 põhimõtteid. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Proceedings of the Estonian Academy of Sciences is the property of Teaduste Akadeemia Kirjastus and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. 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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3176/proc.2025.2.26 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 6 StartPage: 228 Subjects: – SubjectFull: Manufacturing execution systems Type: general – SubjectFull: Digital twin Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Digital computer simulation Type: general – SubjectFull: Cluster analysis (Statistics) Type: general Titles: – TitleFull: Virtual factory model development for AI-driven optimization in manufacturing. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Raamets, Tõnis – PersonEntity: Name: NameFull: Karjust, Kristo – PersonEntity: Name: NameFull: Hermaste, Aigar – PersonEntity: Name: NameFull: Kelpman, Karolin IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: 2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 17366046 Numbering: – Type: volume Value: 74 – Type: issue Value: 2 Titles: – TitleFull: Proceedings of the Estonian Academy of Sciences Type: main |
| ResultId | 1 |
