journal article·2018-09-13Open access

Enabling Technologies for Operator 4.0: A Survey

Tamás Ruppert, Szilárd Jaskó, Tibor Holczinger, János Abonyi

Applied Sciences· Volume 8· Issue 9

Abstract

The fast development of smart sensors and wearable devices has provided the opportunity to develop intelligent operator workspaces. The resultant Human-Cyber-Physical Systems (H-CPS) integrate the operators into flexible and multi-purpose manufacturing processes. The primary enabling factor of the resultant Operator 4.0 paradigm is the integration of advanced sensor and actuator technologies and communications solutions. This work provides an extensive overview of these technologies and highlights that the design of future workplaces should be based on the concept of intelligent space.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Digital Transformation in Industry · Flexible and Reconfigurable Manufacturing Systems · Human-Automation Interaction and Safety

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

MTA-PE “Lendület” Complex Systems Monitoring Research Group, Department of Process Engineering, University of Pannonia, Egyetem u. 10, POB 158, H-8200 Veszprém, Hungary

Department of Applied Informatics, Nagykanizsa Campus, University of Pannonia, Zrínyi u. 18, H-8800 Nagykanizsa, Hungary

References (100+)

Show source-provided references
1. Industry 4.0-potentials for creating smart products: Empirical research results10.1007/978-3-319-19027-3_2 ↗
2. A review of the meanings and the implications of the Industry 4.0 concept10.1016/j.promfg.2017.09.032 ↗
3. Industry 4.0: State of the art and future trends10.1080/00207543.2018.1444806 ↗
4. Martin, K. (2018). Innovative Competition with Chinese Characteristics. The Case of ’Made in China 2025’ in Relation to the German Industry. [Bachelor’s Thesis]. Available online: https://openaccess.leidenuniv.nl/handle/1887/63733.
5. Shubin, T., and Zhi, P. (2018). “Made in China 2025” and “Industrie 4.0”—In Motion Together. The Internet of Things, Springer.10.1007/978-3-662-54904-9_7 ↗
6. Towards smart factory for industry 4.0: A self-organized multi-agent system with big data based feedback and coordination10.1016/j.comnet.2015.12.017 ↗
7. Chai, X., Hou, B., Zou, P., Zeng, J., and Zhou, J. (2018, January 8–12). INDICS: An Industrial Internet Platform. Proceedings of the IEEE International Conference on Cloud and Big Data Computing, Guangzhou, China.10.1109/SmartWorld.2018.00307 ↗
8. Strategic Plan of “Made in China 2025” and Its Implementation
9. Additive manufacturing and sustainability: An exploratory study of the advantages and challenges10.1016/j.jclepro.2016.04.150 ↗
10. Befähigungs-und Einführungsstrategien für Industrie 4.010.3139/104.111462 ↗
11. Industrie 4.0 Readiness
12. A maturity model for assessing industry 4.0 readiness and maturity of manufacturing enterprises10.1016/j.procir.2016.07.040 ↗
13. Implementing Smart Factory of Industrie 4.0: An Outlook
14. Hawksworth, J., Berriman, R., and Goel, S. (2018, July 20). Will Robots Really Steal Our Jobs? An International Analysis of the Potential Long Term Impact of Automation. Available online: http://pwc.blogs.com/economics_in_business/2018/02/will-robots-really-steal-our-jobs.html.
15. Frey, C.B., and Osborne, M. (2018, July 20). The Future of Employment: How Susceptible Are Jobs to Computerisation. Available online: https://www.oxfordmartin.ox.ac.uk/publications/view/1314.
16. PricewaterhouseCooper (2018, July 20). Workforce of the Future: The Competing Forces Shaping 2030. Available online: https://www.pwc.com/gx/en/services/people-organisation/publications/workforce-of-the-future.html.
17. A model of types and levels of human interaction with automation10.1109/3468.844354 ↗
18. Munir, S., Stankovic, J.A., Liang, C.J.M., and Lin, S. (2013, January 24–28). Cyber Physical System Challenges for Human-in-the-Loop Control. Proceedings of the Presented as part of the 8th International Workshop on Feedback Computing, USENIX, San Jose, CA, USA.
19. Human-automation interaction research: Past, present, and future
20. Wickens, C., Li, H., Santamaria, A., Sebok, A., and Sarter, N. (2010, January 1–3). Stages and levels of automation: An integrated meta-analysis. Proceedings of the Human Factors and Ergonomics Society 54th Annual Meeting, San Francisco, CA, USA.10.1177/154193121005400425 ↗
21. Human related issues in manufacturing cell design, implementation, and operation: A review and survey10.1016/j.cie.2003.03.002 ↗
22. Roitberg, A., Perzylo, A., Somani, N., Giuliani, M., Rickert, M., and Knoll, A. (2014, January 9–12). Human activity recognition in the context of industrial human-robot interaction. Proceedings of the Signal and Information Processing Association Annual Summit and Conference (APSIPA), Siem Reap, Cambodia.10.1109/APSIPA.2014.7041588 ↗
23. Romero, D., Stahre, J., Wuest, T., Noran, O., Bernus, P., Fast-Berglund, Å., and Gorecky, D. (2016, January 29–31). Towards an Operator 4.0 Typology: A Human-Centric Perspective on the Fourth Industrial Revolution Technologies. Proceedings of the International Conference on Computers and Industrial Engineering (CIE46), Tianjin, China.
24. Lorenz, M., Ruessmann, M., Strack, R., Lueth, K.L., and Bolle, M. (2018, July 06). Man and Machine in Industry 4.0: How Will Technology Transform the Industrial Workforce Through 2025. Available online: https://www.bcg.com/publications/2015/technology-business-transformation-engineered-products-infrastructure-man-machine-industry-4.aspx.
25. The Operator 4.0: Human Cyber-Physical Systems & Adaptive Automation Towards Human-Automation Symbiosis Work Systems
26. Past, present and future of Industry 4.0—A systematic literature review and research agenda proposal10.1080/00207543.2017.1308576 ↗
27. Cyber-physical production systems: Roots, expectations and R&D challenges10.1016/j.procir.2014.03.115 ↗
28. Zhou, K., Liu, T., and Zhou, L. (2015, January 15–17). Industry 4.0: Towards future industrial opportunities and challenges. Proceedings of the 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), Zhangjiajie, China.10.1109/FSKD.2015.7382284 ↗
29. Survey on human–robot collaboration in industrial settings: Safety, intuitive interfaces and applications
30. Big data analytics. TDWI Best Practices Report
31. Business intelligence and analytics: From big data to big impact10.2307/41703503 ↗
32. Augmented reality smartphone environment orientation application: A case study of the Fu-Jen University mobile campus touring system10.1016/j.sbspro.2012.05.132 ↗
33. Penttila, K., Pere, N., Sioni, M., Sydanheimo, L., and Kivikoski, M. (2005, January 14–16). Use and interface definition of mobile RFID reader integrated in a smart phone. Proceedings of the Ninth International Symposium on Consumer Electronics, Macau.
34. Smart manufacturing, manufacturing intelligence and demand-dynamic performance10.1016/j.compchemeng.2012.06.037 ↗
35. In-Situ Support for Automotive Manufacturing Using Spatial Augmented Reality10.20870/IJVR.2012.11.1.2835 ↗
36. Spatial augmented reality on industrial CNC-machines. The Engineering Reality of Virtual Reality 2008
37. Augmented reality applications in design and manufacturing10.1016/j.cirp.2012.05.010 ↗
38. Baxter & Sawyer (2018, September 07). Rethink Robotics. Available online: http://www.rethinkrobotics.com/sawyer-intera-3/.
39. An intelligent personal assistant for task and time management
40. Wuest, T., Hribernik, K., and Thoben, K.D. (2012). Can a Product Have a Facebook? A New Perspective on Product Avatars in Product Lifecycle Management. Towards Knowledge-Rich Enterprises, Springer.10.1007/978-3-642-35758-9_36 ↗
41. Ergonomic contribution of ABLE exoskeleton in automotive industry10.1016/j.ergon.2014.03.008 ↗
42. Virtual reality applications in manufacturing process simulation10.1016/j.jmatprotec.2004.04.401 ↗
43. Gait Training With Virtual Reality-Based Real-Time Feedback: Improving Gait Performance Following Transfemoral Amputation10.2522/ptj.20100360 ↗
44. Horváth, L., and Rudas, I.J. (2017, January 26–28). Role of information content in multipurpose virtual engineering space. Proceedings of the 2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI), Her’any, Slovakia.10.1109/SAMI.2017.7880283 ↗
45. Modeling of reconfigurable distributed manufacturing control systems10.1016/j.ifacol.2015.06.262 ↗
46. Virtual Engineering Factory: Creating Experience Base for Industry 4.010.1080/01969722.2016.1128762 ↗
47. The future of manufacturing industry: A strategic roadmap toward Industry 4.010.1108/JMTM-02-2018-0057 ↗
48. Visual Computing as a Key Enabling Technology for Industrie 4.0 and Industrial Internet10.1109/MCG.2015.45 ↗
49. Rüßmann, M., Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Engel, P., and Harnisch, M. (2018, July 20). Industry 4.0: The Future of Productivity and Growth in Manufacturing Industries. Available online: https://www.bcg.com/publications/2015/engineered_products_project_business_industry_4_future_productivity_growth_manufacturing_industries.aspx.
50. Gilchrist, A. (2016). Industry 4.0: The Industrial Internet of Things, Apress.10.1007/978-1-4842-2047-4_10 ↗
51. Business excellence via advanced manufacturing technology and lean-agile manufacturing10.1108/JMTM-03-2017-0049 ↗
52. Pérez Perales, D., Alarcón, F., and Boza, A. (2018). Industry 4.0: A Classification Scheme. Closing the Gap between Practice and Research in Industrial Engineering, Springer International Publishing.10.1007/978-3-319-58409-6_38 ↗
53. Towards a cyber-physical-social-connected and service-oriented manufacturing paradigm: Social Manufacturing10.1016/j.mfglet.2015.12.002 ↗
54. MPDV (2018, July 20). Industry 4.0: MES Supports Decentralization. Available online: https://www.mpdv.com/media/company/company_magazine/NEWS_International_2015.pdf.
55. Virtualisation process of a sheet metal punching machine within the Industry 4.0 vision10.1007/s12008-016-0319-2 ↗
56. Toward Self-Manageable and Adaptive Industrial Cyber-Physical Systems with Knowledge-Driven Autonomic Service Management10.1109/TII.2016.2595401 ↗
57. Feature-based control and information framework for adaptive and distributed manufacturing in cyber physical systems10.1016/j.jmsy.2016.12.003 ↗
58. An Adaptive Control Architecture for Mitigating Sensor and Actuator Attacks in Cyber-Physical Systems10.1109/TAC.2017.2652127 ↗
59. An adaptive control architecture for cyber-physical system security in the face of sensor and actuator attacks and exogenous stochastic disturbances10.1080/23335777.2018.1484818 ↗
60. A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems10.1016/j.mfglet.2014.12.001 ↗
61. Enterprise Ontology Model for Tacit Knowledge Externalization in Socio-Technical Enterprises
62. Polanyi, M. (1992). The Tacit Dimension, University of Chicago Press.
63. The role of tacit and explicit knowledge in the workplace10.1108/13673270110411733 ↗
64. How and why we need to capture tacit knowledge in manufacturing: Case studies of visual inspection10.1016/j.apergo.2018.07.016 ↗
65. Extending hierarchical task analysis to identify cognitive demands andinformation design requirements10.1016/j.apergo.2010.11.009 ↗
66. Task analysis of discrete and continuous skills: A dual methodology approach to human skills capture for automation10.1080/1463922X.2015.1028508 ↗
67. Programming a Robot for Conformance Grinding of Complex Shapes by Capturing the Tacit Knowledge of a Skilled Operator10.1109/TASE.2015.2474708 ↗
68. Development of mathematical models for surface roughness parameter prediction in turning depending on the process condition10.1016/j.ijmecsci.2016.04.015 ↗
69. A dynamic surface topography model for the prediction of nano-surface generation in ultra-precision machining10.1016/S0020-7403(00)00050-3 ↗
70. Floor surface roughness model considering tool vibration in the process of micro-milling10.1007/s00170-017-1123-2 ↗
71. Modelling of surface roughness in inclined milling operations with circle-segment end mills10.1016/j.simpat.2018.02.003 ↗
72. Operator monitoring in a complex dynamic work environment: A qualitative cognitive model based on field observations10.1080/14039220412331298929 ↗
73. Emotion recognition from physiological signals using wireless sensors for presence technologies10.1007/s10111-003-0143-x ↗
74. Online real-time quality monitoring in additive manufacturing processes using heterogeneous sensors
75. A Survey of Quality Prediction of Product Reviews
76. Virtual reality in manufacturing10.1007/978-0-387-35086-8_4 ↗
77. Shiratuddin, M.F., and Zulkifli, A.N. (2001, January 12–14). Virtual reality in manufacturing. Proceedings of the Management Education for the 21st Century, Ho Chi Minh City, Vietnam.
78. Kopácsi, S., and Sárközy, F. (2018, July 20). Virtual Reality in Manufacturing. Available online: http://old.sztaki.hu/~kopacsi/vr/vr_main.htm.
79. A survey of augmented reality10.1162/pres.1997.6.4.355 ↗
80. Caudell, T.P., and Mizell, D.W. (1992, January 7–10). Augmented reality: An application of heads-up display technology to manual manufacturing processes. Proceedings of the Twenty-Fifth Hawaii International Conference on System Sciences, Kauai, HI, USA.10.1109/HICSS.1992.183317 ↗
81. Virtual and Augmented Reality Applications in Manufacturing10.3182/20130619-3-RU-3018.00637 ↗
82. Reengineering aircraft structural life prediction using a digital twin10.1155/2011/154798 ↗
83. Digital twin-driven product design, manufacturing and service with big data10.1007/s00170-017-0233-1 ↗
84. Digital Twin—The Simulation Aspect
85. Algorithms for sequencing mixed models on an assembly line in a JIT production system10.1016/S0360-8352(96)00193-3 ↗
86. A Balancing Problem for Mixed Model Assembly Lines with a Paced Moving Conveyor
87. Properties of a sequencing problem for a mixed model assembly line with conveyor stoppages10.1016/S0377-2217(99)00198-8 ↗
88. Note on Toyota’s goal of sequencing mixed models on an assembly line10.1016/S0360-8352(98)00113-2 ↗
89. A Monte Carlo simulation of human reproduction
90. Raychaudhuri, S. (2008, January 7–10). Introduction to Monte Carlo simulation. Proceedings of the 2008 Winter Simulation Conference, Miami, FL, USA.10.1109/WSC.2008.4736059 ↗
91. Migliaccio, G.C., Cheng, T., Gatti, U.C., and Teizer, J. (2013, January 5–9). Data Fusion of Real-Time Location Sensing (RTLS) and Physiological Status Monitoring (PSM) for Ergonomics Analysis of Construction Workers. Proceedings of the 19th Triennial CIB World Building Congress, Brisbane, Australia.
92. Sensor-based information appliances10.1109/5289.887458 ↗
93. Industrial wearable system: The human-centric empowering technology in Industry 4.0
94. Kong, X.T., Yang, X., Huang, G.Q., and Luo, H. (2018, January 27–29). The impact of industrial wearable system on industry 4.0. Proceedings of the 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC), Zhuhai, China.10.1109/ICNSC.2018.8361266 ↗
95. Survey of wireless indoor positioning techniques and systems10.1109/TSMCC.2007.905750 ↗
96. A survey of indoor positioning systems for wireless personal networks10.1109/SURV.2009.090103 ↗
97. Mautz, R., and Tilch, S. (2011, January 21–23). Survey of optical indoor positioning systems. Proceedings of the 2011 International Conference on Indoor Positioning and Indoor Navigation, Guimaraes, Portugal.10.1109/IPIN.2011.6071925 ↗
98. A standalone RFID indoor positioning system using passive tags10.1109/TIE.2010.2055774 ↗
99. Indoor positioning technologies
100. Kagermann, H., Helbig, J., Hellinger, A., and Wahlster, W. (2013). Recommendations for Implementing the Strategic Initiative INDUSTRIE 4.0: Securing the Future of German Manufacturing Industry, Federal Ministry of Education and Research. Final Report of the Industrie 4.0.10.3390/sci4030026 ↗