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International Journal on Interactive Design and Manufacturing (IJIDeM)
Erschienen in: International Journal on Interactive Design and Manufacturing (IJIDeM) 1/2024

18.10.2023 | Original Paper

Intelligent flexible assembly system for labor-intensive factory using the configurable virtual workstation concept

verfasst von: Sri Raharno, Vina Sari Yosephine

Erschienen in: International Journal on Interactive Design and Manufacturing (IJIDeM) | Ausgabe 1/2024

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Abstract

Joining the digital transformation movement is no longer optional for manufacturing industries. Big corporations are transforming their automated manufacturing systems to improve their planning and operations. However, depending on their product type, a significant portion of the production sectors is labor-intensive or traditional, implying inadequate monitoring and control systems. Thus, they need more technology investment to be ready to join the digital transformation. This study intends to transform such systems by developing an intelligent monitoring and control system framework. We proposed an intelligent assembly system framework based on configurable virtual workstation technology. The solution was tested and implemented in a rolling stock factory. The implementation returns a promising development, enabling us to provide the directions for similar labor-intensive industries to step into the digital transformation era.
Vorheriger Artikel Piezoelectric–electromagnetic integrated vibrational hybrid energy harvester for low power applications
Nächster Artikel Numerical simulation of heat transfer and design optimization of IC engine fins geometry using Finite Element analysis

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Literatur
1.
Raharno, S., Cooper, G: Jumping to Industry 4.0 Through Process Design and Managing Information for Smart Manufacturing: Configurable Virtual Workstation, pp. 47–51 (2020)
2.
Chen, B., Wan, J., Shu, L., Li, P., Mukherjee, M., Yin, B.: Smart factory of industry 4.0: key technologies, application case, and challenges. IEEE Access 6, 6505–6519 (2017)CrossRef
3.
Gruber, F.E.: Industry 4.0: A best practice project of the automotive industry. In: IFIP International Conference on Digital Product and Process Development Systems, pp. 36–40. Springer (2013)
4.
Cemernek, D., Gursch, H., Kern, R.: Big data as a promoter of industry 4.0: lessons of the semiconductor industry. In: 2017 IEEE 15th International Conference on Industrial Informatics (INDIN), pp. 239–244. IEE (2017)
5.
Baheti, R., Gill, H.: Cyber-physical systems. Impact Control Technol. 12(1), 161–166 (2011)
6.
Zhang, D., He, Z., Qian, Y., Wan, J., Li, D., Zhao, S.: Revisiting unknown RFID tag identification in large-scale internet of things. IEEE Wirel. Commun. 23(5), 24–29 (2016)CrossRef
7.
Zhu, X.J.: Semi-supervised Learning Literature Survey (2005)
8.
Wuest, T., Weimer, D., Irgens, C., Thoben, K.-D.: Machine learning in manufacturing: advantages, challenges, and applications. Prod. Manuf. Res. 4(1), 23–45 (2016)
9.
Wang, J., Ma, Y., Zhang, L., Gao, R.X., Wu, D.: Deep learning for smart manufacturing: methods and applications. J. Manuf. Syst. 48, 144–156 (2018)CrossRef
10.
Park, C.Y., Kim, J.W., Kim, B., Lee, J.: Prediction for manufacturing factors in a steel plate rolling smart factory using data clustering-based machine learning. IEEE Access 8, 60890–60905 (2020)CrossRef
11.
Tubaro, P., Casilli, A.A.: Micro-work, artificial intelligence and the automotive industry. J. Ind. Bus. Econ. 46(3), 333–345 (2019)CrossRef
12.
Wu, D., Jennings, C., Terpenny, J., Gao, R.X., Kumara, S.: A comparative study on machine learning algorithms for smart manufacturing: tool wear prediction using random forests. J. Manuf. Sci. Eng. 139(7), 071018 (2017)CrossRef
13.
Ansari, F., Erol, S., Sihn, W.: Rethinking human-machine learning in industry 4.0: How does the paradigm shift treat the role of human learning? Proced. Manuf. 23, 117–122 (2018)
14.
Hartono, R., Raharno, S., Martawirya, Y.Y., Arthaya, B.: Development of product availability monitoring system in production unit in automotive component industry. IOP Publishing 319(1), 012014 (2018)
15.
Hartono, R., Raharno, S., Pane, M.Y., Zulfahmi, M., Yusuf, M., Yuwana, Y., Harja, H.B.: Development of Production Monitoring Systems on Labor-Intensive Manufacturing Industries, pp. 30–34 (2020)
16.
Sudhoff, M., Leineweber, S., Linsinger, M., Niemeyer, J.F., Kuhlenkötter, B.: Objective data acquisition as the basis of digitization in manual assembly systems. Procedia CIRP 93, 1176–1181 (2020)CrossRef
17.
Telukdarie, A., Dube, T., Matjuta, P., Philbin, S.: The opportunities and challenges of digitalization for SMEe’s. Proced. Comput. Sci. 217, 689–698 (2023)CrossRef
18.
Binte Rajah, R., de Fauconberg, A., Woeffray, O.: Future Readiness of SMEs: Mobilizing the SME Sector to Drive Widespread Sustainability and Prosperity. World Economic Forum (2021)
19.
Tarallo, A., Mozzillo, R., Di Gironimo, G., De Amicis, R.: A cyber-physical system for production monitoring of manual manufacturing processes. Int. J. Interact. Design Manuf. (IJIDeM) 12, 1235–1241 (2018)CrossRef
20.
Strandhagen, J.W., Alfnes, E., Strandhagen, J.O., Vallandingham, L.R.: The fit of industry 4.0 applications in manufacturing logistics: a multiple case study. Adv. Manuf. 5(4), 344–358 (2017)CrossRef
21.
Yurdakul, M., İç, Y.T., Celek, O.E.: Design of the Assembly Systems for Airplane Structures, pp. 521–541 (2021)
22.
Lee, J., Han, S.: Knowledge-based configuration design of a train bogie. J. Mech. Sci. Technol. 24(12), 2503–2510 (2010)CrossRef
23.
Lee, J., Bagheri, B., Kao, H.-A.: A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manuf. Lett. 3, 18–23 (2015)CrossRef
24.
Xu, X., Hua, Q.: Industrial big data analysis in smart factory: current status and research strategies. IEEE Access 5, 17543–17551 (2017)CrossRef
25.
Hozdić, E.: Smart factory for industry 4.0: a review. Int. J. Modern Manuf. Technol. 7(1), 28–35 (2015)
26.
Li, L.: China’s manufacturing locus in 2025: with a comparison of “Made-in-China 2025" and “Industry 4.0". Technol. Forecast. Soc. Change 135, 66–74 (2018)
27.
Tavassoli, S., Kianian, B., Larsson, T.C.: Manufacturing Renaissance: Return of Manufacturing to Western Countries, pp. 261–280 (2015)
28.
Schwab, K.: The Fourth Industrial Revolution (2017)
29.
Monostori, L., Kádár, B., Bauernhansl, T., Kondoh, S., Kumara, S., Reinhart, G., Sauer, O., Schuh, G., Sihn, W., Ueda, K.: Cyber-physical systems in manufacturing. Cirp Annals 65(2), 621–641 (2016)CrossRef
30.
Biesinger, F., Meike, D., Kraß, B., Weyrich, M.: A digital twin for production planning based on cyber-physical systems: a case study for a cyber-physical system-based creation of a digital twin. Procedia CIRP 79, 355–360 (2019)CrossRef
31.
Lee, E.A.: Cyber physical systems: design challenges. In: 2008 11th IEEE International Symposium on Object and Component-oriented Real-time Distributed Computing (ISORC), pp. 363–369. IEEE (2008) IEEE
32.
Shu, Z., Wan, J., Zhang, D., Li, D.: Cloud-integrated cyber-physical systems for complex industrial applications. Mob. Netw. Appl. 21(5), 865–878 (2016)CrossRef
33.
Krugh, M., Mears, L.: A complementary cyber-human systems framework for industry 4.0 cyber-physical systems. Manuf. Lett. 15, 89–92 (2018)CrossRef
34.
Lee, J., Kao, H.-A., Yang, S.: Service innovation and smart analytics for industry 4.0 and big data environment. Procedia Cirp 16, 3–8 (2014)CrossRef
35.
Jazdi, N.: Cyber physical systems in the context of industry 4.0. In: 2014 IEEE International Conference on Automation, Quality and Testing, Robotics, pp. 1–4. IEE (2014)
36.
Mo, J.P.: The role of lean in the application of information technology to manufacturing. Comput. Ind. 60(4), 266–276 (2009)CrossRef
37.
Amaral, A., Peças, P.: SMEs and industry 40: two case studies of digitalization for a smoother integration. Comput. Ind. 125, 103333 (2021)CrossRef
38.
Amaral, A., Peças, P.: A framework for assessing manufacturing SMEs industry 4.0 maturity. Appl. Sci. 11(13), 6127 (2021)CrossRef
39.
Charania, I., Li, X.: Smart farming: agriculture’s shift from a labor intensive to technology native industry. Internet Things 9, 100142 (2020)CrossRef
40.
Seseni, L., Mbohwa, C.: Fourth industrial revolution and its impact on furniture manufacturing SMEs. In: Proceedings of the International Conference on Industrial Engineering and Operations Management, pp. 2572–2577 (2018)
41.
Kim, J.-C., Moon, I.-Y.: A study on smart factory construction method for efficient production management in sewing industry. J. Inf. Commun. Converg. Eng. 18(1), 61–68 (2020)
42.
Gökalp, E., Gökalp, M.O., Eren, P.E.: Industry 4.0 revolution in clothing and apparel factories: apparel 4.0. Ind. 4.0 Manag. Inf. Syst. Perspect. 4, 169–184 (2018)
43.
Schumacher, A., Nemeth, T., Sihn, W.: Roadmapping towards industrial digitalization based on an industry 4.0 maturity model for manufacturing enterprises. Proced. Cirp 79, 409–414 (2019)CrossRef
44.
Ganzarain, J., Errasti, N.: Three stage maturity model in SME’s toward industry 4.0. J. Ind. Eng. Manag. (JIEM) 9(5), 1119–1128 (2016)
45.
Unver, H.O.: An ISA-95-based manufacturing intelligence system in support of lean initiatives. Int. J. Adv. Manuf. Technol. 65(5), 853–866 (2013)CrossRef
46.
Raharno, S., Hartono, R., Martawirya, Y.Y.: Implementing industry 4.0 in an Indonesia traditional assembly industry: Manufacturing system. In: Proceedings of the 16th International Conference on Automation Technology (2019)
47.
48.
Ward, M.J., Halliday, S.T., Foden, J.: A readiness level approach to manufacturing technology development in the aerospace sector: an industrial approach. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 226(3), 547–552 (2012)CrossRef
49.
Eswaran, M., Bahubalendruni, M.R.: Challenges and opportunities on AR/VR technologies for manufacturing systems in the context of industry 4.0: a state of the art review. J. Manuf. Syst. 65, 260–278 (2022)
Metadaten
Titel
Intelligent flexible assembly system for labor-intensive factory using the configurable virtual workstation concept
verfasst von
Sri Raharno
Vina Sari Yosephine
Publikationsdatum
18.10.2023
Verlag
Springer Paris
Erschienen in
International Journal on Interactive Design and Manufacturing (IJIDeM) / Ausgabe 1/2024
Print ISSN: 1955-2513
Elektronische ISSN: 1955-2505
DOI
https://doi.org/10.1007/s12008-023-01567-3

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