A Learning Factory Implementation with Industry, Teaching and Research Perspectives
In the last decade, Industry 4.0 has resulted in widespread change in how manufacturing operates and, as a consequence, the skills set that a graduating engineer is required to display. This rapid change has resulted in challenges for educational institutions at third level as mechanical and manufac...
Gespeichert in:
| Veröffentlicht in: | Procedia computer science Jg. 253; S. 1093 - 1102 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier B.V
2025
|
| Schlagworte: | |
| ISSN: | 1877-0509, 1877-0509 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | In the last decade, Industry 4.0 has resulted in widespread change in how manufacturing operates and, as a consequence, the skills set that a graduating engineer is required to display. This rapid change has resulted in challenges for educational institutions at third level as mechanical and manufacturing engineering has now become a interdisciplinary profession requiring not only traditional skills but also skills in information technology, computer science, among others. The challenge in an educational setting is to develop a space where various disciplines can work together, both educators and students in an industrially relevant space. For this reason, a learning factory has been developed in the Munster Technological University with that purpose in mind. Following up on previous work, this paper presents achievements in educational, industrial interaction and research projects. The learning factory has permitted the development of masters level modules, provide training for local industry and also research in the area of machine learning, digital twin, and autonomous flexible manufacturing systems, projects which are summarised in this paper. The paper ends with a discussion of the future direction of the learning factory with the objective of sustainable growth. |
|---|---|
| AbstractList | In the last decade, Industry 4.0 has resulted in widespread change in how manufacturing operates and, as a consequence, the skills set that a graduating engineer is required to display. This rapid change has resulted in challenges for educational institutions at third level as mechanical and manufacturing engineering has now become a interdisciplinary profession requiring not only traditional skills but also skills in information technology, computer science, among others. The challenge in an educational setting is to develop a space where various disciplines can work together, both educators and students in an industrially relevant space. For this reason, a learning factory has been developed in the Munster Technological University with that purpose in mind. Following up on previous work, this paper presents achievements in educational, industrial interaction and research projects. The learning factory has permitted the development of masters level modules, provide training for local industry and also research in the area of machine learning, digital twin, and autonomous flexible manufacturing systems, projects which are summarised in this paper. The paper ends with a discussion of the future direction of the learning factory with the objective of sustainable growth. |
| Author | Ozturk, Cemalettin Ahearn, Aaron Casotti, Alberto Buckley, Killian Quinn, William |
| Author_xml | – sequence: 1 givenname: William surname: Quinn fullname: Quinn, William email: william.quinn@mtu.ie organization: School of Mechanical, Electrical and Process Engineering, Munster Technological University, Cork, Ireland – sequence: 2 givenname: Aaron surname: Ahearn fullname: Ahearn, Aaron organization: School of Mechanical, Electrical and Process Engineering, Munster Technological University, Cork, Ireland – sequence: 3 givenname: Killian surname: Buckley fullname: Buckley, Killian organization: School of Mechanical, Electrical and Process Engineering, Munster Technological University, Cork, Ireland – sequence: 4 givenname: Alberto surname: Casotti fullname: Casotti, Alberto organization: School of Mechanical, Electrical and Process Engineering, Munster Technological University, Cork, Ireland – sequence: 5 givenname: Cemalettin surname: Ozturk fullname: Ozturk, Cemalettin organization: School of Mechanical, Electrical and Process Engineering, Munster Technological University, Cork, Ireland |
| BookMark | eNp9kMFOAjEQQBuDiYh8gZd-gLu2XUu3Bw-EiJKQaBTPTbedSgl0N-2K4e8t4sGTc5mZZN5k5l2iQWgDIHRNSUkJndxuyi62JpWMMF4SWlJBz9CQ1kIUhBM5-FNfoHFKG5KjqmtJxRC9TfESdAw-fOC5Nn0bD3ix67awg9Dr3rcBf_l-jRfBfqY-Hm7wCrRZH8d1sPgVUqbNGr9ATB2Y3u8hXaFzp7cJxr95hN7nD6vZU7F8flzMpsvCMMppIZicMO6kJDa3RgrCwQlnGya5c5ZUsrZ3DRO8NnVtiebEVZpry5uqkUzKaoSq014T25QiONVFv9PxoChRRzVqo37UqKMaRajKajJ1f6Ign7b3EFUyHoIB62N-QNnW_8t_AzM4b84 |
| Cites_doi | 10.1016/j.promfg.2020.04.105 10.1016/j.promfg.2020.04.069 10.1109/UEMCON53757.2021.9666556 10.1088/1757-899X/844/1/012024 10.1109/ICIRCA48905.2020.9183342 10.1109/ECTI-CON54298.2022.9795561 10.1016/j.procir.2019.03.002 10.1016/j.promfg.2017.09.039 10.1016/j.promfg.2019.03.019 10.1016/j.promfg.2017.04.023 10.1080/0951192X.2015.1033017 10.1016/j.procir.2016.06.051 10.1109/ICIT45562.2020.9067188 10.1080/00207543.2021.1956675 10.1016/j.procir.2015.02.187 10.1007/978-3-642-35950-7_16828-1 10.1016/j.compind.2020.103261 10.1016/j.ifacol.2022.09.471 10.1109/CVPR42600.2020.01079 10.1016/j.promfg.2020.04.066 10.1177/09504222221100343 10.1109/NITC48475.2019.9114507 10.1109/MELECON53508.2022.9843084 |
| ContentType | Journal Article |
| Copyright | 2025 |
| Copyright_xml | – notice: 2025 |
| DBID | 6I. AAFTH AAYXX CITATION |
| DOI | 10.1016/j.procs.2025.01.171 |
| DatabaseName | ScienceDirect Open Access Titles Elsevier:ScienceDirect:Open Access CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1877-0509 |
| EndPage | 1102 |
| ExternalDocumentID | 10_1016_j_procs_2025_01_171 S1877050925001796 |
| GroupedDBID | --K 0R~ 1B1 457 5VS 6I. 71M AAEDT AAEDW AAFTH AAIKJ AALRI AAQFI AAXUO AAYWO ABMAC ABWVN ACGFS ACRPL ACVFH ADBBV ADCNI ADEZE ADNMO ADVLN AEUPX AEXQZ AFPUW AFTJW AGHFR AIGII AITUG AKBMS AKRWK AKYEP ALMA_UNASSIGNED_HOLDINGS AMRAJ E3Z EBS EJD EP3 FDB FNPLU HZ~ IXB KQ8 M41 M~E O-L O9- OK1 P2P RIG ROL SES SSZ 9DU AAYXX CITATION ~HD |
| ID | FETCH-LOGICAL-c2151-729625f990d151c9705ef7fdb295ffd0398d4b2758c88d0a50f3a5ad5b3b92993 |
| ISSN | 1877-0509 |
| IngestDate | Thu Nov 27 01:01:44 EST 2025 Sat Aug 09 17:31:36 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Distributed Wireless Networked Control Systems Energy Efficient Manufacturing Learning Factory Digital Twin |
| Language | English |
| License | This is an open access article under the CC BY-NC-ND license. |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c2151-729625f990d151c9705ef7fdb295ffd0398d4b2758c88d0a50f3a5ad5b3b92993 |
| OpenAccessLink | https://dx.doi.org/10.1016/j.procs.2025.01.171 |
| PageCount | 10 |
| ParticipantIDs | crossref_primary_10_1016_j_procs_2025_01_171 elsevier_sciencedirect_doi_10_1016_j_procs_2025_01_171 |
| PublicationCentury | 2000 |
| PublicationDate | 2025 2025-00-00 |
| PublicationDateYYYYMMDD | 2025-01-01 |
| PublicationDate_xml | – year: 2025 text: 2025 |
| PublicationDecade | 2020 |
| PublicationTitle | Procedia computer science |
| PublicationYear | 2025 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Abele, E., 2016. Learning Factory. Springer Berlin Heidelberg, Berlin, Heidelberg. pp. 1–5. URL McShane, J., Meehan, K., Furey, E., McAfee, M., 2021. Classifying plastic waste on river surfaces utilising cnn and tensorflow, in: 2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), IEEE. pp. 0475–0481. SimPy, 2023. python-snap7 wrapper. URL Andersen, A.L., Brunoe, T.D., Nielsen, K., 2019. Engineering education in changeable and reconfigurable manufacturing: Using problem-based learning in a learning factory environment. Procedia CIRP 81, 7–12. Gateway, 2023. Gateway (ngr-30-3). URL Zhang, W., Cai, W., Min, J., Fleischer, J., Ehrmann, C., Prinz, C., Kreimeier, D., 2020. 5G and AI technology application in the AMTC learning factory, in: Procedia Manufacturing, Elsevier B.V. pp. 66–71. Louw, L., Deacon, Q., 2020. Teaching industrie 4.0 technologies in a learning factory through problem-based learning: Case study of a semi-automated robotic cell design. Procedia Manufacturing 45, 265–270. Quinn, W., Cionca, V., Witheephanich, K., Ozturk, C., 2022. A learning factory framework: Challenges and solutions for an irish university*. IFAC-PapersOnLine 55, 631–636. doi Kushal, M., BV, K.K., MJ, C.K., Pappa, M., 2020. Id card detection with facial recognition using tensorflow and opencv, in: 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA), IEEE. pp. 742–746. Tisch, M., Hertle, C., Abele, E., Metternich, J., Tenberg, R., 2016. Learning factory design: a competency-oriented approach integrating three design levels. International Journal of Computer Integrated Manufacturing 29, 1355–1375. Tan, M., Pang, R., Le, Q.V., 2020. Efficientdet: Scalable and efficient object detection, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 10781–10790. IFM, 2023. Vse002/vse100 software. URL Zancul, E., Martins, H.O., Lopes, F.P., da Silva Neto, F.A., 2020. Machine vision applications in a learning factory. Procedia Manufacturing 45, 516–521. Sanchez, S., Romero, H., Morales, A., 2020. A review: Comparison of performance metrics of pretrained models for object detection using the tensorflow framework, in: IOP Conference Series: Materials Science and Engineering, IOP Publishing. p. 012024. Wijesinghe, L., Kulasekera, D., Ilmini, W., 2019. An intelligent approach to segmentation and classification of common skin diseases in sri lanka, in: 2019 National Information Technology Conference (NITC), IEEE. pp. 47–52. Abele, E., Bauerdick, C.J., Strobel, N., Panten, N., 2016. Eta learning factory: A holistic concept for teaching energy efficiency in production. Procedia CIRP 54, 83–88. ISA, 2023. Beyond the pyramid: Using isa95 for industry 4.0 and smart manufacturing. URL Fernández-Miranda, S.S., Marcos, M., Peralta, M., Aguayo, F., 2017. The challenge of integrating industry 4.0 in the degree of mechanical engineering. Procedia Manufacturing 13, 1229–1236. URL Masood, T., Sonntag, P., 2020. Industry 4.0: Adoption challenges and benefits for smes. Computers in industry 121, 103261. Boonkong, A., Hormdee, D., Sonsilphong, S., Khampitak, K., 2022. Surgical instrument detection for laparoscopic surgery using deep learning, in: 2022 19th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), IEEE. pp. 1–4. Episensor, 2023. Wireless 3-phase electricity monitor (zem-63). URL Azangoo, M., Blech, J.O., Atmojo, U.D., 2020. Towards formal monitoring of workpieces in agile manufacturing, in: Proceedings of the IEEE International Conference on Industrial Technology, Institute of Electrical and Electronics Engineers Inc.. pp. 334–339. distfit, 2023. distfit probability density fitting of univariate distributions for random variables. URL Bellucci, M., Chiurco, A., Cimino, A., Ferro, D., Longo, F., Padovano, A., 2022. Learning factories: a review of state of the art and development of a morphological model for an industrial engineering education 4.0, in: 2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON), pp. 260–265. doi 10th IFAC Conference on Manufacturing Modelling, Management and Control MIM 2022. Simons, S., Abé, P., Neser, S., 2017. Learning in the autfab–the fully automated industrie 4.0 learning factory of the university of applied sciences darmstadt. Procedia Manufacturing 9, 81–88. MORI, D., 2023. distfit probability density fitting of univariate distributions for random variables. URL . Rai, R., Tiwari, M.K., Ivanov, D., Dolgui, A., 2021. Machine learning in manufacturing and industry 4.0 applications. International Journal of Production Research 59, 4773–4778. python snap7, 2023. Simpy: Simulation framework in python. URL manufacturing Engineering Society International Conference 2017, MESIC 2017, 28-30 June 2017, Vigo (Pontevedra), Spain. Salvador, R., Barros, M.V., Barreto, B., Pontes, J., Yoshino, R.T., Piekarski, C.M., de Francisco, A.C., 2023. Challenges and opportunities for problem-based learning in higher education: Lessons from a cross-program industry 4.0 case. Industry and Higher Education 37, 3–21. URL: https://doi.org/10.1177/09504222221100343, doi:10.1177/09504222221100343, arXiv Singh, I., Centea, D., Elbestawi, M., 2019. Iot, iiot and cyber-physical systems integration in the sept learning factory. Procedia Manufacturing 31, 116–122. Abele, E., Metternich, J., Tisch, M., Chryssolouris, G., Sihn, W., ElMaraghy, H., Hummel, V., Ranz, F., 2015. Learning factories for research, education, and training. Procedia CIRP 32, 1–6. doi 10.1016/j.procs.2025.01.171_bib1 10.1016/j.procs.2025.01.171_bib5 10.1016/j.procs.2025.01.171_bib4 10.1016/j.procs.2025.01.171_bib3 10.1016/j.procs.2025.01.171_bib2 10.1016/j.procs.2025.01.171_bib30 10.1016/j.procs.2025.01.171_bib11 10.1016/j.procs.2025.01.171_bib12 10.1016/j.procs.2025.01.171_bib31 10.1016/j.procs.2025.01.171_bib10 10.1016/j.procs.2025.01.171_bib15 10.1016/j.procs.2025.01.171_bib16 10.1016/j.procs.2025.01.171_bib13 10.1016/j.procs.2025.01.171_bib14 10.1016/j.procs.2025.01.171_bib19 10.1016/j.procs.2025.01.171_bib17 10.1016/j.procs.2025.01.171_bib18 10.1016/j.procs.2025.01.171_bib22 10.1016/j.procs.2025.01.171_bib23 10.1016/j.procs.2025.01.171_bib20 10.1016/j.procs.2025.01.171_bib21 10.1016/j.procs.2025.01.171_bib9 10.1016/j.procs.2025.01.171_bib26 10.1016/j.procs.2025.01.171_bib8 10.1016/j.procs.2025.01.171_bib27 10.1016/j.procs.2025.01.171_bib7 10.1016/j.procs.2025.01.171_bib24 10.1016/j.procs.2025.01.171_bib6 10.1016/j.procs.2025.01.171_bib25 10.1016/j.procs.2025.01.171_bib28 10.1016/j.procs.2025.01.171_bib29 |
| References_xml | – reference: Louw, L., Deacon, Q., 2020. Teaching industrie 4.0 technologies in a learning factory through problem-based learning: Case study of a semi-automated robotic cell design. Procedia Manufacturing 45, 265–270. – reference: Simons, S., Abé, P., Neser, S., 2017. Learning in the autfab–the fully automated industrie 4.0 learning factory of the university of applied sciences darmstadt. Procedia Manufacturing 9, 81–88. – reference: Abele, E., 2016. Learning Factory. Springer Berlin Heidelberg, Berlin, Heidelberg. pp. 1–5. URL: – reference: Tan, M., Pang, R., Le, Q.V., 2020. Efficientdet: Scalable and efficient object detection, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 10781–10790. – reference: Salvador, R., Barros, M.V., Barreto, B., Pontes, J., Yoshino, R.T., Piekarski, C.M., de Francisco, A.C., 2023. Challenges and opportunities for problem-based learning in higher education: Lessons from a cross-program industry 4.0 case. Industry and Higher Education 37, 3–21. URL: https://doi.org/10.1177/09504222221100343, doi:10.1177/09504222221100343, arXiv: – reference: Singh, I., Centea, D., Elbestawi, M., 2019. Iot, iiot and cyber-physical systems integration in the sept learning factory. Procedia Manufacturing 31, 116–122. – reference: Andersen, A.L., Brunoe, T.D., Nielsen, K., 2019. Engineering education in changeable and reconfigurable manufacturing: Using problem-based learning in a learning factory environment. Procedia CIRP 81, 7–12. – reference: Episensor, 2023. Wireless 3-phase electricity monitor (zem-63). URL: – reference: Zhang, W., Cai, W., Min, J., Fleischer, J., Ehrmann, C., Prinz, C., Kreimeier, D., 2020. 5G and AI technology application in the AMTC learning factory, in: Procedia Manufacturing, Elsevier B.V. pp. 66–71. – reference: Fernández-Miranda, S.S., Marcos, M., Peralta, M., Aguayo, F., 2017. The challenge of integrating industry 4.0 in the degree of mechanical engineering. Procedia Manufacturing 13, 1229–1236. URL: – reference: manufacturing Engineering Society International Conference 2017, MESIC 2017, 28-30 June 2017, Vigo (Pontevedra), Spain. – reference: 10th IFAC Conference on Manufacturing Modelling, Management and Control MIM 2022. – reference: SimPy, 2023. python-snap7 wrapper. URL: – reference: Abele, E., Metternich, J., Tisch, M., Chryssolouris, G., Sihn, W., ElMaraghy, H., Hummel, V., Ranz, F., 2015. Learning factories for research, education, and training. Procedia CIRP 32, 1–6. – reference: ISA, 2023. Beyond the pyramid: Using isa95 for industry 4.0 and smart manufacturing. URL: – reference: Abele, E., Bauerdick, C.J., Strobel, N., Panten, N., 2016. Eta learning factory: A holistic concept for teaching energy efficiency in production. Procedia CIRP 54, 83–88. – reference: Boonkong, A., Hormdee, D., Sonsilphong, S., Khampitak, K., 2022. Surgical instrument detection for laparoscopic surgery using deep learning, in: 2022 19th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), IEEE. pp. 1–4. – reference: Masood, T., Sonntag, P., 2020. Industry 4.0: Adoption challenges and benefits for smes. Computers in industry 121, 103261. – reference: python snap7, 2023. Simpy: Simulation framework in python. URL: – reference: Sanchez, S., Romero, H., Morales, A., 2020. A review: Comparison of performance metrics of pretrained models for object detection using the tensorflow framework, in: IOP Conference Series: Materials Science and Engineering, IOP Publishing. p. 012024. – reference: Zancul, E., Martins, H.O., Lopes, F.P., da Silva Neto, F.A., 2020. Machine vision applications in a learning factory. Procedia Manufacturing 45, 516–521. – reference: Azangoo, M., Blech, J.O., Atmojo, U.D., 2020. Towards formal monitoring of workpieces in agile manufacturing, in: Proceedings of the IEEE International Conference on Industrial Technology, Institute of Electrical and Electronics Engineers Inc.. pp. 334–339. – reference: Quinn, W., Cionca, V., Witheephanich, K., Ozturk, C., 2022. A learning factory framework: Challenges and solutions for an irish university*. IFAC-PapersOnLine 55, 631–636. doi: – reference: Bellucci, M., Chiurco, A., Cimino, A., Ferro, D., Longo, F., Padovano, A., 2022. Learning factories: a review of state of the art and development of a morphological model for an industrial engineering education 4.0, in: 2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON), pp. 260–265. doi: – reference: Wijesinghe, L., Kulasekera, D., Ilmini, W., 2019. An intelligent approach to segmentation and classification of common skin diseases in sri lanka, in: 2019 National Information Technology Conference (NITC), IEEE. pp. 47–52. – reference: . – reference: Gateway, 2023. Gateway (ngr-30-3). URL: – reference: IFM, 2023. Vse002/vse100 software. URL: – reference: Rai, R., Tiwari, M.K., Ivanov, D., Dolgui, A., 2021. Machine learning in manufacturing and industry 4.0 applications. International Journal of Production Research 59, 4773–4778. – reference: McShane, J., Meehan, K., Furey, E., McAfee, M., 2021. Classifying plastic waste on river surfaces utilising cnn and tensorflow, in: 2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), IEEE. pp. 0475–0481. – reference: Tisch, M., Hertle, C., Abele, E., Metternich, J., Tenberg, R., 2016. Learning factory design: a competency-oriented approach integrating three design levels. International Journal of Computer Integrated Manufacturing 29, 1355–1375. – reference: MORI, D., 2023. distfit probability density fitting of univariate distributions for random variables. URL: – reference: distfit, 2023. distfit probability density fitting of univariate distributions for random variables. URL: – reference: , doi: – reference: Kushal, M., BV, K.K., MJ, C.K., Pappa, M., 2020. Id card detection with facial recognition using tensorflow and opencv, in: 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA), IEEE. pp. 742–746. – ident: 10.1016/j.procs.2025.01.171_bib15 doi: 10.1016/j.promfg.2020.04.105 – ident: 10.1016/j.procs.2025.01.171_bib30 doi: 10.1016/j.promfg.2020.04.069 – ident: 10.1016/j.procs.2025.01.171_bib17 doi: 10.1109/UEMCON53757.2021.9666556 – ident: 10.1016/j.procs.2025.01.171_bib22 doi: 10.1088/1757-899X/844/1/012024 – ident: 10.1016/j.procs.2025.01.171_bib8 – ident: 10.1016/j.procs.2025.01.171_bib14 doi: 10.1109/ICIRCA48905.2020.9183342 – ident: 10.1016/j.procs.2025.01.171_bib7 doi: 10.1109/ECTI-CON54298.2022.9795561 – ident: 10.1016/j.procs.2025.01.171_bib4 doi: 10.1016/j.procir.2019.03.002 – ident: 10.1016/j.procs.2025.01.171_bib10 doi: 10.1016/j.promfg.2017.09.039 – ident: 10.1016/j.procs.2025.01.171_bib25 doi: 10.1016/j.promfg.2019.03.019 – ident: 10.1016/j.procs.2025.01.171_bib23 doi: 10.1016/j.promfg.2017.04.023 – ident: 10.1016/j.procs.2025.01.171_bib28 doi: 10.1080/0951192X.2015.1033017 – ident: 10.1016/j.procs.2025.01.171_bib11 – ident: 10.1016/j.procs.2025.01.171_bib2 doi: 10.1016/j.procir.2016.06.051 – ident: 10.1016/j.procs.2025.01.171_bib13 – ident: 10.1016/j.procs.2025.01.171_bib5 doi: 10.1109/ICIT45562.2020.9067188 – ident: 10.1016/j.procs.2025.01.171_bib20 doi: 10.1080/00207543.2021.1956675 – ident: 10.1016/j.procs.2025.01.171_bib3 doi: 10.1016/j.procir.2015.02.187 – ident: 10.1016/j.procs.2025.01.171_bib1 doi: 10.1007/978-3-642-35950-7_16828-1 – ident: 10.1016/j.procs.2025.01.171_bib9 – ident: 10.1016/j.procs.2025.01.171_bib16 doi: 10.1016/j.compind.2020.103261 – ident: 10.1016/j.procs.2025.01.171_bib26 – ident: 10.1016/j.procs.2025.01.171_bib24 – ident: 10.1016/j.procs.2025.01.171_bib19 doi: 10.1016/j.ifacol.2022.09.471 – ident: 10.1016/j.procs.2025.01.171_bib27 doi: 10.1109/CVPR42600.2020.01079 – ident: 10.1016/j.procs.2025.01.171_bib31 doi: 10.1016/j.promfg.2020.04.066 – ident: 10.1016/j.procs.2025.01.171_bib21 doi: 10.1177/09504222221100343 – ident: 10.1016/j.procs.2025.01.171_bib29 doi: 10.1109/NITC48475.2019.9114507 – ident: 10.1016/j.procs.2025.01.171_bib12 – ident: 10.1016/j.procs.2025.01.171_bib18 – ident: 10.1016/j.procs.2025.01.171_bib6 doi: 10.1109/MELECON53508.2022.9843084 |
| SSID | ssj0000388917 |
| Score | 2.3424442 |
| Snippet | In the last decade, Industry 4.0 has resulted in widespread change in how manufacturing operates and, as a consequence, the skills set that a graduating... |
| SourceID | crossref elsevier |
| SourceType | Index Database Publisher |
| StartPage | 1093 |
| SubjectTerms | Digital Twin Distributed Wireless Networked Control Systems Energy Efficient Manufacturing Learning Factory |
| Title | A Learning Factory Implementation with Industry, Teaching and Research Perspectives |
| URI | https://dx.doi.org/10.1016/j.procs.2025.01.171 |
| Volume | 253 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1877-0509 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000388917 issn: 1877-0509 databaseCode: M~E dateStart: 20100101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1LT9tAEF4F2gMXSnmIR0F76C2x5Oz6sXu0UKNeQJGSStysje1tE4GDkoDgwg_gVzOzj8QtESqVerHiTTaOZr7MjMcz3xDylUVVpJkWQQL_pCBKOQsklzrgkQL3w4oYtplhE-nlpbi6kv1W69n3wtxfp3UtHh7k7X9VNayBsrF19h3qXn4pLMBrUDocQe1w_CvFZ54z9We7Z4bpPFoK4BvXZWT7tttuZoeR8dCXVNpuRZcJ6a_6MOfNGNb0FgCsTDk6ToRoOze6SqKObVTskjlLUP3ytcOZmq0e_7vHy8bkmA3Ld87VfLqw9QYZcnEtps0khW1ltjmzV30zxsyKNA2QecZ6oTVrzjazmDesK1JfNTw1RC5srRewCYkJ-qACKdmZoWbt2lkvf9BrD_C6eFmIBdE6JRvkA0tjieWBF0-rfB2y5kgzwHn5Qz2JlSkXfHWt9YFOI3gZ7pBtd9dBM4uWz6RV1bvkk5_oQZ2B3yODjHrwUAce-jt4KIKHevB0qIcOBehQDx3ahM4--dH7Njz_HrixG0GB8R8ON4abYg1hSgmnhQTpVDrV5YjJWOsy5FKU0QhkJAohylDFoeYqVmU84iMItiU_IJv1tK4OCU2QUU4rJRPBIwjm4WM6SrqVLHSZKBYekY6XUX5r2VVyX3Y4yY1IcxRpHnZzEOkRSbwcc4dsG_jloPm3Nh7_68YTsoVnNuf2hWwuZnfVKflY3C_G89mZQcgLHNGL2w |
| linkProvider | ISSN International Centre |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+Learning+Factory+Implementation+with+Industry%2C+Teaching+and+Research+Perspectives&rft.jtitle=Procedia+computer+science&rft.au=Quinn%2C+William&rft.au=Ahearn%2C+Aaron&rft.au=Buckley%2C+Killian&rft.au=Casotti%2C+Alberto&rft.date=2025&rft.pub=Elsevier+B.V&rft.issn=1877-0509&rft.eissn=1877-0509&rft.volume=253&rft.spage=1093&rft.epage=1102&rft_id=info:doi/10.1016%2Fj.procs.2025.01.171&rft.externalDocID=S1877050925001796 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1877-0509&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1877-0509&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1877-0509&client=summon |