Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives

Frontiers of Mechanical Engineering

Volumn 13, Issue 2, 2018, Pages 137-150

  Zheng, Pai ^a   wang, Honghui ^a   Sang, Zhiqian ^a   Zhong, Ray Y ^a   Liu, Yongkui ^a   Liu, Chao ^a   Mubarok, Khamdi ^a   Yu, Shiqiang ^a   Xu, Xun ^a  

^a UNIVERSITY OF AUCKLAND   (New Zealand)

Author keywords

big data analytics; cyber physical systems; framework; Industry 4.0; Internet of Things; smart manufacturing systems

Indexed keywords

EID: 85040866293     PISSN: 20950233     EISSN: 20950241     Source Type: Journal    
DOI: 10.1007/s11465-018-0499-5     Document Type: Review

References (72)

1. Rittinghouse J W, Ransome J F. Cloud Computing: Implementation, Management, and Security. Boca Raton: CRC Press, 2016
2. Zhang Y F, Zhang G, Wang J Q, et al. Real-time information capturing and integration framework of the internet of manufacturing things. International Journal of Computer Integrated Manufacturing, 2015, 28(8): 811–822
3. Liu C, Jiang P. A cyber-physical system architecture in shop floor for intelligent manufacturing. Procedia CIRP, 2016, 56: 372–377
4. Kagermann H, Helbig J, Hellinger A, et al. Recommendations for implementing the strategic initiative INDUSTRIE 4.0: Securing the future of German manufacturing industry; final report of the Industrie 4.0 Working Group. Forschungsunion, 2013
5. Liu Y, Xu X. Industry 4.0 and cloud manufacturing: A comparative analysis. Journal of Manufacturing Science and Engineering, 2016, 139(3): 034701
6. Lu Y. Industry 4.0: A survey on technologies, applications and open research issues. Journal of Industrial Information Integration, 2017, 6: 1–10
7. Thames L, Schaefer D. Industry 4.0: An overview of key benefits, technologies, and challenges. In: Thames L, Schaefer D, eds. Cybersecurity for Industry 4.0. Cham: Springer, 2017, 1–33
8. Kusiak A. Smart manufacturing. International Journal of Production Research, 2017, 1–10 (in press)
9. Penas O, Plateaux R, Patalano S, et al. Multi-scale approach from mechatronic to cyber-physical systems for the design of manufacturing systems. Computers in Industry, 2017, 86: 52–69
10. Zawadzki P, Żywicki K. Smart product design and production control for effective mass customization in the Industry 4.0 concept. Management and Production Engineering Review, 2016, 7(3): 105–112
11. Bokrantz J, Skoogh A, Berlin C, et al. Maintenance in digitalised manufacturing: Delphi-based scenarios for 2030. International Journal of Production Economics, 2017, 191: 154–169
12. Xia T, Xi L. Manufacturing paradigm-oriented PHM methodologies for cyber-physical systems. Journal of Intelligent Manufacturing, 2017, 1–14 (in press)
13. Xu X. Machine Tool 4.0 for the new era of manufacturing. International Journal of Advanced Manufacturing Technology, 2017, 1–8 (in press)
14. Nienke S, Frölian H, Zeller V, et al. Energy-Management 4.0: Roadmap towards the self-optimising production of the future. In: Proceedings of the 6th International Conference on Informatics, Environment, Energy and Applications. 2017, 6–10
15. Hofmann E, Rüsch M. Industry 4.0 and the current status as well as future prospects on logistics. Computers in Industry, 2017, 89: 23–34
16. Kolarevic B. Architecture in the digital age: Design and manufacturing. Abingdon: Taylor & Francis, 2004
17. Zhong R Y, Dai Q Y, Qu T, et al. RFID-enabled real-time manufacturing execution system for mass-customization production. Robotics and Computer-integrated Manufacturing, 2013, 29 (2): 283–292
18. Park H S, Tran N H. Development of a smart machining system using self-optimizing control. International Journal of Advanced Manufacturing Technology, 2014, 74(9–12): 1365–1380
19. Janak L, Hadas Z. Machine tool health and usage monitoring system: An intitial analyses. MM Science Journal, 2015, 2015(4): 794–798
20. Qiu X, Luo H, Xu G Y, et al. Physical assets and service sharing for IoT-enabled supply hub in industrial park (SHIP). International Journal of Production Economics, 2015, 159: 4–15
21. Wang M L, Qu T, Zhong R Y, et al. A radio frequency identification-enabled real-time manufacturing execution system for one-of-a-kind production manufacturing: A case study in mould industry. International Journal of Computer Integrated Manufacturing, 2012, 25(1): 20–34
22. Stich V, Hering N, Meißner J. Cyber physical production control: Transparency and high resolution in production control. IFIP Advances in Information and Communication Technology, 2015, 459: 308–315
23. Makarov O, Langmann R, Nesteresko S, et al. Problems of the time deterministic in applications for process control from the cloud. International Journal of Online Engineering, 2014, 10(4): 70–73
24. Wang L H. Machine availability monitoring and machining process planning towards cloud manufacturing. CIRP Journal of Manufacturing Science and Technology, 2013, 6(4): 263–273
25. Wu D Z, Rosen D W, Wang L H, et al. Cloud-based design and manufacturing: A new paradigm in digital manufacturing and design innovation. Computer Aided Design, 2015, 59: 1–14
26. Marzband M, Parhizi N, Savaghebi M, et al. Distributed smart decision-making for a multimicrogrid system based on a hierarchical interactive architecture. IEEE Transactions on Energy Conversion, 2016, 31(2): 637–648
27. Büyüközkan G, Güleryüz S. Multi criteria group decision making approach for smart phone selection using intuitionistic fuzzy TOPSIS. International Journal of Computational Intelligence Systems, 2016, 9(4): 709–725
28. Papakostas N, Efthymiou K, Georgoulias K, et al. On the configuration and planning of dynamic manufacturing networks. In: Papakostas N, Efthymiou K, Georgoulias K, et al., eds. Logistics Research. Berlin: Springer, 2012, 5(3–4): 105–111
29. Messina G, Morici L, Celentano G, et al. REBCO coils system for axial flux electrical machines application: Manufacturing and testing. IEEE Transactions on Applied Superconductivity, 2016, 26(3): 1–4
30. Rajalingam S, Malathi V. HEM algorithm based smart controller for home power management system. Energy and Building, 2016, 131: 184–192
31. Javed A, Larijani H, Ahmadinia A, et al. Smart random neural network controller for HVAC using cloud computing technology. IEEE Transactions on Industrial Informatics, 2016, (99): 1–11
32. Zhong R Y, Huang G Q, Lan S, et al. A two-level advanced production planning and scheduling model for RFID-enabled ubiquitous manufacturing. Advanced Engineering Informatics, 2015, 29(4): 799–812
33. Wang X V, Xu X W. A collaborative product data exchange environment based on STEP. International Journal of Computer Integrated Manufacturing, 2015, 28(1): 75–86
34. Zhong R Y, Li Z, Pang A L Y, et al. RFID-enabled real-time advanced planning and scheduling shell for production decisionmaking. International Journal of Computer Integrated Manufacturing, 2013, 26(7): 649–662
35. Zhong R Y, Newman S T, Huang G Q, et al. Big data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectives. Computers & Industrial Engineering, 2016, 101: 572–591
36. Zhang L, Luo Y, Tao F, et al. Cloud manufacturing: A new manufacturing paradigm. Enterprise Information Systems, 2014, 8 (2): 167–187
37. Xu X. From cloud computing to cloud manufacturing. Robotics and Computer-integrated Manufacturing, 2012, 28(1): 75–86
38. Zhong R Y, Huang G Q, Lan S L, et al. A big data approach for logistics trajectory discovery from RFID-enabled production data. International Journal of Production Economics, 2015, 165: 260–272
39. Lee J, Kao H A, Yang S. Service innovation and smart analytics for Industry 4.0 and big data environment. Procedia CIRP, 2014, 16: 3–8
40. Cochran D S, Kinard D, Bi Z. Manufacturing system design meets big data analytics for continuous improvement. Procedia CIRP, 2016, 50: 647–652
41. Niesen T, Houy C, Fettke P, et al. Towards an integrative big data analysis framework for data-driven risk management in Industry 4.0. In: Proceedings of 2016 49th Hawaii International Conference on System Sciences (HICSS). Hawaii, 2016, 5065–5074
42. Babiceanu R F, Seker R. Big data and virtualization for manufacturing cyber-physical systems: A survey of the current status and future outlook. Computers in Industry, 2016, 81: 128–137
43. Zhong R Y, Lan S, Xu C, et al. Visualization of RFID-enabled shopfloor logistics big data in cloud manufacturing. International Journal of Advanced Manufacturing Technology, 2016, 84(1–4): 5–16
44. O’Donovan P, Leahy K, Bruton K, et al. Big data in manufacturing: A systematic mapping study. Journal of Big Data, 2015, 2: 20
45. Lee J, Bagheri B, Kao H A. A cyber-physical systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters, 2015, 3: 18–23
46. DIS. ISO. 9241–210: 2010. Ergonomics of human system interaction- Part 210: Human-centred design for interactive systems. International Standardization Organization (ISO), 2009
47. Tseng M M, Jiao R J, Wang C. Design for mass personalization. CIRP Annals-Manufacturing Technology, 2010, 59(1): 175–178
48. Schmidt R, Möhring M, Härting R C, et al. Industry 4.0—Potentials for creating smart products: Empirical research results. In: International Conference on Business Information Systems. 2015, 16–27
49. Zheng P, Yu S, Wang Y, et al. User-experience based product development for mass personalization: A case study. Procedia CIRP, 2017, 63: 2–7
50. Gu P, Xue D, Nee A Y C. Adaptable design: Concepts, methods, and applications. Proceedings of the Institution of Mechanical Engineers. Part B, Journal of Engineering Manufacture, 2009, 223 (11): 1367–1387
51. Liu A, Lu S C Y. A new coevolution process for conceptual design. CIRP Annals-Manufacturing Technology, 2015, 64(1): 153–156
52. Chen X, Wang Y, Yin Z. RFID based production and distribution management systems for home appliance industry. In: Proceedings of 2010 IEEE International Conference on Automation and Logistics (ICAL). Hong Kong and Macau, 2010, 177–182
53. Lee E A. Cyber physical systems: Design challenges. In: Proceedings of 2008 11th IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing (ISORC). IEEE, 2008, 363–369
54. MTConnect Institute. MTConnect Standard. Part 1—Overview and protocol. Version 1.0.1. 2009. Retrieved from https://static1.squarespace. com/static/54011775e4b0bc1fe0fb8494/t/55800405e4b057 e97372fe59/1434452997276/MTC_Part_1_Overview_v1.0.1R10_02_09.pdf
55. Pinedo M. Scheduling—Theory, Algorithms, and Systems. New York: Springer, 2015
56. Tang L, Zhang Y. Parallel machine scheduling under the disruption of machine breakdown. Industrial & Engineering Chemistry Research, 2009, 48(14): 6660–6667
57. Sanlaville E, Schmidt G. Machine scheduling with availability constraints. Acta Informatica, 1998, 35(9): 795–811
58. Ivanov D, Dolgui A, Sokolov B, et al. A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory Industry 4.0. International Journal of Production Research, 2016, 54(2): 386–402
59. Lee J, Bagheri B, Kao H A. A cyber-physical systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters, 2015, 3: 18–23
60. Attanasio A, Ghiani G, Grandinetti L, et al. Auction algorithms for decentralized parallel machine scheduling. Parallel Computing, 2006, 32(9): 701–709
61. Wong T, Leung C, Mak K L, et al. Dynamic shopfloor scheduling in multi-agent manufacturing systems. Expert Systems with Applications, 2006, 31(3): 486–494
62. Xiang W, Lee H. Ant colony intelligence in multi-agent dynamic manufacturing scheduling. Engineering Applications of Artificial Intelligence, 2008, 21(1): 73–85
63. Adeyeri MK, Mpofu K, Adenuga Olukorede T. Integration of agent technology into manufacturing enterprise: A review and platform for Industry 4.0. In: Proceedings of IEOM 2015 5th International Conference on Industrial Engineering and Operations Management. 2015
64. Glück M, Wolf J. Integrated quality management for Industry 4.0. Productivity Management, 2014, 19: 19–22
65. Wells L J, Shafae M S, Camelio J A. Automated part inspection using 3D point clouds. In: Proceedings of ASME 2013 International Manufacturing Science and Engineering Conference Collocated with the 41st North American Manufacturing Research Conference. 2013, V002T02A034
66. McAfee S T, Greene W J. US Patents 20120290259,–11-15
67. Dai Q Y, Zhong R Y, Huang G Q, et al. Radio frequency identification-enabled real-time manufacturing execution system: A case study in an automotive part manufacturer. International Journal of Computer Integrated Manufacturing, 2012, 25(1): 51–65
68. Pang L Y, Li Z, Huang G Q, et al. Auto-ID enabled reconfigurable SaaS shell for real-time fleet management in industrial parks. Journal of Computing in Civil Engineering, 2013, 29(2): 04014032
69. Nee A, Ong S, Chryssolouris G, et al. Augmented reality applications in design and manufacturing. CIRP Annals-Manufacturing Technology, 2012, 61(2): 657–679
70. Jin X, Zong S, Li Y, et al. A domain knowledge based method on active and focused information service for decision support within big data environment. Procedia Computer Science, 2015, 60: 93–102
71. Zhong R Y, Huang G Q, Dai Q Y, et al. Mining SOTs and dispatching rules from RFID-enabled real-time shopfloor production data. Journal of Intelligent Manufacturing, 2014, 25(4): 825–843
72. Li B H, Zhang L, Wang S L, et al. Cloud manufacturing: A new service-oriented networked manufacturing model. Computer Integrated Manufacturing Systems, 2010, 16(1): 1–7, 16 (in Chinese)