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

doi:10.1007/s11465-018-0499-5

Front. Mech. Eng.

2018, Vol. 13

Issue (2) : 137-150 https://doi.org/10.1007/s11465-018-0499-5

REVIEW ARTICLE

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

Pai ZHENG, Honghui WANG, Zhiqian SANG, Ray Y. ZHONG(

), Yongkui LIU, Chao LIU, Khamdi MUBAROK, Shiqiang YU, Xun XU

Department of Mechanical Engineering, University of Auckland, Auckland, New Zealand

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Abstract

Information and communication technology is undergoing rapid development, and many disruptive technologies, such as cloud computing, Internet of Things, big data, and artificial intelligence, have emerged. These technologies are permeating the manufacturing industry and enable the fusion of physical and virtual worlds through cyber-physical systems (CPS), which mark the advent of the fourth stage of industrial production (i.e., Industry 4.0). The widespread application of CPS in manufacturing environments renders manufacturing systems increasingly smart. To advance research on the implementation of Industry 4.0, this study examines smart manufacturing systems for Industry 4.0. First, a conceptual framework of smart manufacturing systems for Industry 4.0 is presented. Second, demonstrative scenarios that pertain to smart design, smart machining, smart control, smart monitoring, and smart scheduling, are presented. Key technologies and their possible applications to Industry 4.0 smart manufacturing systems are reviewed based on these demonstrative scenarios. Finally, challenges and future perspectives are identified and discussed.

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

Corresponding Author(s): Ray Y. ZHONG

Just Accepted Date: 27 December 2017 Online First Date: 24 January 2018 Issue Date: 16 March 2018

Cite this article:

Pai ZHENG,Honghui WANG,Zhiqian SANG, et al. Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives[J]. Front. Mech. Eng., 2018, 13(2): 137-150.

URL:

https://academic.hep.com.cn/fme/EN/10.1007/s11465-018-0499-5
https://academic.hep.com.cn/fme/EN/Y2018/V13/I2/137

Fig.1 Conceptual framework of Industry 4.0 smart manufacturing systems

Fig.2 Conceptual framework of the proposed product development process

Fig.3 CPS-enabled smart machine tools

Fig.4 Energy-efficient manufacturing

Fig.5 Cloud-based smart control system

Fig.6 Machine scheduling in Industry 4.0

Fig.7 Smart 3D scanning for automated quality inspection

Scenarios	Advantages	Disadvantages
UX-based personalized smart wearable device	• Users are actively involved in the co-creation process for personalization. • User experience can be readily obtained/analyzed in a real-time design context. • Product change can be rapidly prototyped for design innovation in a cyber-physical manner.	• The application scope of the model is limited to highly modularized or discreet manufacturing systems (e.g., automobile and bicycles), rather than integral or continuous processes (e.g., chemical process and natural gas).
CPS-based smart machine tools	• Users can control the machine tool in real time by using cloud-based services. • Real-time status can be reflected in the user interface.	• System reliability is based on the stability of communication networks. • Information confidentiality is an issue on the part of end users.
Energy consumption monitoring	• Energy consumption can be tracked and visualized in real time. • Decision making/optimization can be based on energy consumption.	• Smart sensors should be equipped to machines. • Data transmission relies on multiple channels.
Cloud-based numerical control	• Control of the machine is servicelized. • Highly sophisticated algorisms can be applied. • Service is flexible and can be updated and upgraded easily. • The process know-hows can be well protected.	• Concerns on cyber security and service availability may exist.
Machine scheduling in smart factories	• Machines are optimally scheduled based on real-time information. • Any disturbances can be tracked and traced in real time.	• Advanced decision-making models are required. • Real-time data processing models are necessary.
Smart 3D scanning for automated quality inspection	• Quality inspection can be automatically executed. • Quality data can be visualized in real time for decision making.	• Data storage and processing may be an issue if the volume of real-time information is large.

Tab.1 Summary of demonstrative scenarios

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 https://doi.org/10.1080/0951192X.2014.900874
3	Liu C, Jiang P. A cyber-physical system architecture in shop floor for intelligent manufacturing. Procedia CIRP, 2016, 56: 372–377 https://doi.org/10.1016/j.procir.2016.10.059
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 https://doi.org/10.1115/1.4034667
6	Lu Y. Industry 4.0: A survey on technologies, applications and open research issues. Journal of Industrial Information Integration, 2017, 6: 1–10 https://doi.org/10.1016/j.jii.2017.04.005
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) https://doi.org/10.1080/00207543.2017.1351644
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 https://doi.org/10.1016/j.compind.2016.12.001
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 https://doi.org/10.1515/mper-2016-0030
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 https://doi.org/10.1016/j.ijpe.2017.06.010
12	Xia T, Xi L. Manufacturing paradigm-oriented PHM methodologies for cyber-physical systems. Journal of Intelligent Manufacturing, 2017, 1–14 (in press) https://doi.org/10.1007/s10845-017-1342-2
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 https://doi.org/10.1016/j.compind.2017.04.002
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 https://doi.org/10.1016/j.rcim.2012.08.001
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 https://doi.org/10.1007/s00170-014-6076-0
19	Janak L, Hadas Z. Machine tool health and usage monitoring system: An intitial analyses. MM Science Journal, 2015, 2015(4): 794–798 https://doi.org/10.17973/MMSJ.2015_12_201564
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 https://doi.org/10.1016/j.ijpe.2014.09.001
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 https://doi.org/10.1080/0951192X.2011.575183
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 https://doi.org/10.3991/ijoe.v10i4.3832
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 https://doi.org/10.1016/j.cirpj.2013.07.001
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 https://doi.org/10.1016/j.cad.2014.07.006
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 https://doi.org/10.1109/TEC.2015.2505358
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 https://doi.org/10.1080/18756891.2016.1204119
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 https://doi.org/10.1109/TASC.2016.2552158
30	Rajalingam S, Malathi V. HEM algorithm based smart controller for home power management system. Energy and Building, 2016, 131: 184–192 https://doi.org/10.1016/j.enbuild.2016.09.026
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 https://doi.org/10.1109/TII.2016.2597746
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 https://doi.org/10.1016/j.aei.2015.01.002
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 https://doi.org/10.1080/0951192X.2013.785028
34	Zhong R Y, Li Z, Pang A L Y, et al. RFID-enabled real-time advanced planning and scheduling shell for production decision-making. International Journal of Computer Integrated Manufacturing, 2013, 26(7): 649–662 https://doi.org/10.1080/0951192X.2012.749532
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 https://doi.org/10.1016/j.cie.2016.07.013
36	Zhang L, Luo Y, Tao F, et al. Cloud manufacturing: A new manufacturing paradigm. Enterprise Information Systems, 2014, 8(2): 167–187 https://doi.org/10.1080/17517575.2012.683812
37	Xu X. From cloud computing to cloud manufacturing. Robotics and Computer-integrated Manufacturing, 2012, 28(1): 75–86 https://doi.org/10.1016/j.rcim.2011.07.002
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 https://doi.org/10.1016/j.ijpe.2015.02.014
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 https://doi.org/10.1016/j.procir.2014.02.001
40	Cochran D S, Kinard D, Bi Z. Manufacturing system design meets big data analytics for continuous improvement. Procedia CIRP, 2016, 50: 647–652 https://doi.org/10.1016/j.procir.2016.05.004
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 https://doi.org/10.1016/j.compind.2016.02.004
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 https://doi.org/10.1007/s00170-015-7702-1
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 https://doi.org/10.1016/j.mfglet.2014.12.001
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 https://doi.org/10.1016/j.cirp.2010.03.097
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 https://doi.org/10.1016/j.procir.2017.03.122
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 https://doi.org/10.1243/09544054JEM1387
51	Liu A, Lu S C Y. A new coevolution process for conceptual design. CIRP Annals-Manufacturing Technology, 2015, 64(1): 153–156 https://doi.org/10.1016/j.cirp.2015.04.020
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
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 https://doi.org/10.1021/ie801868f
57	Sanlaville E, Schmidt G. Machine scheduling with availability constraints. Acta Informatica, 1998, 35(9): 795–811 https://doi.org/10.1007/s002360050143
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 https://doi.org/10.1080/00207543.2014.999958
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 https://doi.org/10.1016/j.mfglet.2014.12.001
60	Attanasio A, Ghiani G, Grandinetti L, et al. Auction algorithms for decentralized parallel machine scheduling. Parallel Computing, 2006, 32(9): 701–709 https://doi.org/10.1016/j.parco.2006.03.002
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 https://doi.org/10.1016/j.eswa.2005.09.073
62	Xiang W, Lee H. Ant colony intelligence in multi-agent dynamic manufacturing scheduling. Engineering Applications of Artificial Intelligence, 2008, 21(1): 73–85 https://doi.org/10.1016/j.engappai.2007.03.008
63	Adeyeri M K, 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, 2012-11-15
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 https://doi.org/10.1080/0951192X.2011.562546
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 https://doi.org/10.1061/(ASCE)CP.1943-5487.0000306
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 https://doi.org/10.1016/j.cirp.2012.05.010
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 https://doi.org/10.1016/j.procs.2015.08.108
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 https://doi.org/10.1007/s10845-012-0721-y
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)

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