The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
Smart manufacturing process and system automation – A critical review of the standards and envisioned scenarios
Abstract
Abstract Smart manufacturing is arriving. It promises a future of mass-producing highly personalized products via responsive autonomous manufacturing operations at a competitive cost. Of utmost importance, smart manufacturing requires end-to-end integration of intra-business and inter-business manufacturing processes and systems. Such end-to-end integration relies on standards-compliant and interoperable interfaces between different manufacturing stages and systems. In this paper, we present a comprehensive review of the current landscape of manufacturing automation standards, with a focus on end-to-end integrated manufacturing processes and systems towards mass personalization and responsive factory automation. First, we present an authentic vision of smart manufacturing and the unique needs for next-generation manufacturing automation. A comprehensive review of existing standards for enabling manufacturing process automation and manufacturing system automation is presented. Subsequently, focusing on meeting changing demands of efficient production of highly personalized products, we detail several future-proofing manufacturing automation scenarios via integrating various existing standards. We believe that existing automation standards have provided a solid foundation for developing smart manufacturing solutions. Faster, broader and deeper implementation of smart manufacturing automation can be anticipated via the dissemination, adoption, and improvement of relevant standards in a need-driven approach.
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Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357
A Comprehensive Survey of Multiagent Reinforcement Learning
Busoniu, L., Babuska, R., & De Schutter, B. (2008) · IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) · doi:10.1109/tsmcc.2007.913919 · Cited by 2,235
The wisdom hierarchy: representations of the DIKW hierarchy
Rowley, J. (2007) · Journal of Information Science · doi:10.1177/0165551506070706 · Cited by 1,827
From cloud computing to cloud manufacturing
Xu, X. (2012) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2011.07.002 · Cited by 1,780
Self-citationWollschlaeger, M., Sauter, T., & Jasperneite, J. (2017) · IEEE Industrial Electronics Magazine · doi:10.1109/mie.2017.2649104 · Cited by 1,726
Lu, Y., Liu, C., Wang, K. I.-K., Huang, H., & Xu, X. (2020) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2019.101837 · Cited by 1,624
Self-citationKusiak, A. (2018) · International Journal of Production Research · doi:10.1080/00207543.2017.1351644 · Cited by 1,330
Cooperative Multi-Agent Learning: The State of the Art
Panait, L., & Luke, S. (2005) · Autonomous Agents and Multi-Agent Systems · doi:10.1007/s10458-005-2631-2 · Cited by 1,237
China's manufacturing locus in 2025: With a comparison of “Made-in-China 2025” and “Industry 4.0”
Li, L. (2018) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2017.05.028 · Cited by 1,073
A review: Knowledge reasoning over knowledge graph
Chen, X., Jia, S., & Xiang, Y. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.112948 · Cited by 1,035
Cited by
Past, present, and future barriers to digital transformation in manufacturing: A review
Jones, M. D., Hutcheson, S., & Camba, J. D. (2021) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2021.03.006 · Cited by 377
Hybrid learning-based digital twin for manufacturing process: modeling framework and implementation
Huang, Z., Fey, M., Liu, C., Beysel, E., Xu, X., & Brecher, C. (2023) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2023.102545 · Cited by 117
Self-citationPeruzzini, M., Prati, E., & Pellicciari, M. (2024) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2023.2257634 · Cited by 100
Adaptive reconstruction of digital twins for machining systems: A transfer learning approach
Liu, S., Lu, Y., Zheng, P., Shen, H., & Bao, J. (2022) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2022.102390 · Cited by 90
Self-citationJing, H., Zhang, Y., & Ma, J. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e21020 · Cited by 35
Metadata
- Authors
- Lu, Y., Xu, X., & Wang, L.
- Venue
- Journal of Manufacturing Systems
- Publisher
- Elsevier BV
- Published
- 2020-07
- Volume
- 56
- Pages
- 312-325
- ISSN
- 0278-6125 (ISSN-L)
- OpenAlex
- W3039095521
- Cited by
- 528
- References
- 45
- FWCI
- 43.95
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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