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

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

References

[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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, RFID-enabled real-time advanced planning and scheduling shell for production decision-making. International Journal of Computer Integrated Manufacturing, 2013, 26(7): 649–662

[35] Zhong R Y, Newman S T, Huang G Q, 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, 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, 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, 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, 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, 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, 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, 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, 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, Auction algorithms for decentralized parallel machine scheduling. Parallel Computing, 2006, 32(9): 701–709

[61] Wong T, Leung C, Mak K L, 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 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, 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, 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, Augmented reality applications in design and manufacturing. CIRP Annals-Manufacturing Technology, 2012, 61(2): 657–679

[70] Jin X, Zong S, Li Y, 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, 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, Cloud manufacturing: A new service-oriented networked manufacturing model. Computer Integrated Manufacturing Systems, 2010, 16(1): 1–7, 16 (in Chinese)

RIGHTS & PERMISSIONS

2018 Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature