Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1
Wagner Augusto Aranda Cotta
 -- 1496 2023-11-01 13:05:58 |
2 format correct
Catherine Yang
 Meta information modification 1496 2023-11-02 01:58:17 |

Video Upload Options

We provide professional Video Production Services to translate complex research into visually appealing presentations. Would you like to try it?
No, upload directly Yes

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Cotta, W.A.A.; Lopes, S.I.; Vassallo, R.F. Towards the Cognitive Factory in Industry 5.0. Encyclopedia. Available online: https://encyclopedia.pub/entry/51044 (accessed on 19 November 2024).
Cotta WAA, Lopes SI, Vassallo RF. Towards the Cognitive Factory in Industry 5.0. Encyclopedia. Available at: https://encyclopedia.pub/entry/51044. Accessed November 19, 2024.
Cotta, Wagner Augusto Aranda, Sérgio Ivan Lopes, Raquel Frizera Vassallo. "Towards the Cognitive Factory in Industry 5.0" Encyclopedia, https://encyclopedia.pub/entry/51044 (accessed November 19, 2024).
Cotta, W.A.A., Lopes, S.I., & Vassallo, R.F. (2023, November 01). Towards the Cognitive Factory in Industry 5.0. In Encyclopedia. https://encyclopedia.pub/entry/51044
Cotta, Wagner Augusto Aranda, et al. "Towards the Cognitive Factory in Industry 5.0." Encyclopedia. Web. 01 November, 2023.
Towards the Cognitive Factory in Industry 5.0
Edit
This entry is adapted from the peer-reviewed paper 10.3390/smartcities6040088

Industry 5.0 (I5.0) represents a shift towards a human-centered industry and emphasizes the integration of human and machine capabilities. A highly compatible concept for enabling the I5.0 implementation is intelligent spaces (ISs), i.e., physical spaces equipped with a network of sensors, which obtains information about the place it observes, and a network of actuators, which enables changes in the environment through computing services. These spaces can sense, interpret, recognize user behavior, adapt to preferences, and provide natural interactions between humans and intelligent systems, using the IoT, AI, computer vision, data analytics, etc., to create dynamic and adaptive environments in real time.

Industry 5.0 cognitive factory intelligent space cyberphysical human-centered systems

1. Introduction

The rapid advancement of Industry 4.0 (I4.0) technologies has brought many changes and benefits to the manufacturing sector [1]. However, these technological advancements also brought some negative impacts on human beings. A point of concern that can be mentioned is the potential displacement of human workers due to automation and the adoption of artificial intelligence (AI)-based systems [2]. This displacement leads to job losses and economic inequalities, as individuals may struggle to adapt to the changing skill requirements in the labor market [3][4]. Thus, the industry and the academia started the transition from I4.0 to Industry 5.0 (I5.0), which goes beyond technological advancements and brings a new paradigm shift in manufacturing processes, with the goal of creating a human-centered industry by integrating human workers and advanced robotic systems to enhance productivity and collaboration, creating a new era of intelligent, interconnected systems [5][6][7][8][9].
Alongside the I4.0 technologies such as the Internet of things (IoT) [10] and its variants (the green IoT [11][12] and industrial IoT [13][14]), big data analytics, [15] and machine learning algorithms [16], this transition, illustrated in Figure 1, is enabled by the implementation of I5.0’s main building blocks: individualized human–machine interaction technologies that interconnect and combine the strengths of humans and machines; bioinspired technologies and smart materials that allow for materials with embedded sensors and enhanced features while being recyclable; real-time-based digital twins and simulation to model entire systems; data transmission, storage, and analysis technologies that are able to handle data and system interoperability; AI to detect, for example, failures in complex and dynamic systems, leading to actionable intelligence; and technologies for energy efficiency, renewables, storage and autonomy [17]. This results in an interconnected manufacturing ecosystem that enables the industry to respond to this paradigm shift.
Figure 1. I5.0 ecosystem with human–centered technologies.
I5.0 presents a human-centric transformative concept and brings a holistic development perspective that aims to foster a flourishing manufacturing system, embracing the integration of advanced technologies, collaborative human–machine interaction, and a focus on long-term viability and social well-being [18]. In I5.0, humans are not replaced by machines; instead, they work alongside robots and intelligent systems to leverage their respective strengths [19]. This collaboration allows more complex and creative tasks, with humans providing critical thinking, problem-solving, and adaptability, while machines contribute with precision, speed, and repetitive task execution [20].
Moreover, a highly compatible concept for enabling the I5.0 implementation is the intelligent space (IS). An IS, represented in Figure 2, can be defined as a physical space equipped with a network of sensors which obtains information about the place it observes, a network of actuators which allows its interaction with users, and changes in the environment through computing services. Such sensors, actuators, and computing services must be governed by an infrastructure capable of collecting and analyzing the information obtained by the sensors and making decisions [21][22][23]. One of the key concepts within ISs is that the environment can sense, interpret, recognize user behavior, adapt to preferences, and provide natural interactions between humans and intelligent systems, using the IoT, AI, computer vision, and data analytics to create dynamic and adaptive environments in real time.
Figure 2. IS composed by a network of sensors and a network of actuators.
The integration of ISs and I5.0 has paved the way for the development of cognitive factories, marking a significant milestone in the evolution of manufacturing. The cognitive factory idea is not new and can be defined as a physical industrial environment transformed into an IS that can perceive, learn, and interact with humans and machines in a seamless and adaptive manner [24][25][26]. These factories can leverage the technologies already mentioned and illustrated in Figure 1 to enable real-time data collection and analysis, allowing predictive maintenance and process optimization continuously. In addition, by integrating cyberphysical systems (CPS), i.e., the convergence of operational technology (OT) and information technology (IT), they blur the boundaries between physical and digital systems, enhancing the communication and coordination between machines, humans, and products, resulting in a more agile and responsive manufacturing ecosystem [27].
Since a cognitive factory can be composed of smaller aggregated units containing sensors, devices, systems, and subsystems, integrated or not, in this article, these smaller aggregated units are denominated cognitive cells, and the definition is stated as a fundamental component that operates as an intelligent unit equipped with cognitive technologies to enhance manufacturing processes. These cells represent a significant departure from traditional automation and digitalization approaches. While automation and digitalization focus on enhancing efficiency and productivity through the use of advanced technologies, the cognitive cell goes beyond that by incorporating cognitive capabilities and human–machine collaboration. Unlike traditional systems, the cognitive cell leverages AI, edge/fog computing, and advanced sensing capabilities to enable an adaptive behavior. By integrating human intelligence and problem-solving skills with machine precision and speed, the cognitive cell creates a dynamic and interactive environment where humans and machines work together.

2. Industry 5.0

The transition from I4.0 to I5.0 has garnered significant attention in recent years as technological advancements continue to reshape the industrial landscape and represent a significant paradigm shift in the global industrial landscape. Extensive research has been conducted to explore the aspects and implications of this technological progression. Scholars such as [28][29] have examined the potential benefits of I5.0, emphasizing its ability to foster greater human–machine collaboration and enhance productivity. Additionally, the study developed by [30] has delved into the challenges and barriers associated with this transition, including the need for upskilling the workforce and ensuring the ethical use of emerging technologies.
Moreover, researchers have investigated the key technological components that underpin the transition from I4.0 to I5.0. For instance, [31] conducted an in-depth survey on enabling technologies and their findings highlight the potential use of edge computing, digital twins, cobots, the Internet of Everything (IoE), big data analytics, blockchain, and future 6G systems. In a similar vein, [32] explored the significance of big data with AI in I5.0. The study revealed how AI can augment decision-making processes, optimize production workflows, and enable adaptive and autonomous systems.
Scholars also consider that the transition to I5.0 is already underway. For example, the study by [33] proposed a cyberphysical human-centered system (CPHS) that utilized a hybrid edge computing architecture with smart mist-computing nodes to process thermal images and prevent industrial safety issues. In the work, the authors demonstrated in a real-world scenario that the developed CPHS algorithms could accurately detect human presence with low-power devices in a fast and efficient manner, complying with the I5.0 paradigm.
In the context of I5.0, the integration and coordination of interconnected systems play a vital role in achieving a consistent data flow and efficient resource utilization. Extensive research has been conducted to investigate the interconnection of edge devices and cloud infrastructure to create a distributed computing ecosystem. Scholars have proposed some architectural frameworks and protocols for enabling the integration and collaboration of these systems [33][34][35].
Edge computing has emerged as a promising paradigm to address the requirements since the arrival of I4.0. Researchers have recognized the significance of these computing models in enabling efficient data processing and analytics at the network edge. Studies have shown that edge computing facilitates real-time data processing and reduces network latency by moving computation closer to data sources [36][37][38]. Although not focused on I5.0 but rather on I4.0, [39] proposed an edge-computing-based industrial gateway for interfacing IT and OT that could enable I4.0’s vertical and horizontal integration.
Similarly, fog computing extends the capabilities of edge computing by providing a decentralized computing infrastructure that bridges the gap between edge devices and the cloud, enabling data processing, storage, and analysis closer to the network edge [40][41].
Besides those mentioned technologies, [42] proposed a communication architecture based on a message bus to enable not only communication to cross conventional physical borders but also to provide scalability to growing data volume and network sizes. The author argued that such architecture had the potential to renew industrial communications. At last, [43] presented a new middleware broker communication architecture for protocols used by the IoT. 
In conclusion, the literature review on the communication architecture for cognitive factories reveals an important role in supporting the integration and operation of cognitive technologies in manufacturing environments. The studies demonstrated the importance of designing efficient frameworks that enable real-time data exchange, support collaboration, and ensure the interoperability within cognitive factory systems. By addressing the challenges and considerations associated with the communication architecture, researchers can advance the development and implementation of cognitive factories, paving the way for future advancements in intelligent manufacturing systems.

References

  1. Schwab, K. The Fourth Industrial Revolution; Crown: New York, NY, USA, 2017; ISBN 978-1-5247-5887-5.
  2. Brynjolfsson, E.; McAfee, A. The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies; W. W. Norton & Company: New York, NY, USA, 2014; ISBN 978-0-393-24125-9.
  3. Davenport, T.H.; Kirby, J. Only Humans Need Apply: Winners and Losers in the Age of Smart Machines; HarperCollins: New York, NY, USA, 2016; ISBN 978-0-06-243860-7.
  4. Acemoglu, D.; Restrepo, P. Automation and New Tasks: How Technology Displaces and Reinstates Labor. J. Econ. Perspect. 2019, 33, 3–30.
  5. Bi, Z.M.; Luo, C.; Miao, Z.; Zhang, B.; Zhang, W.J.; Wang, L. Safety assurance mechanisms of collaborative robotic systems in manufacturing. Robot. Comput.-Integr. Manuf. 2021, 67, 102022.
  6. Nahavandi, S. Industry 5.0—A Human-Centric Solution. Sustainability 2019, 11, 4371.
  7. Sadrfaridpour, B.; Wang, Y. Collaborative Assembly in Hybrid Manufacturing Cells: An Integrated Framework for Human–Robot Interaction. IEEE Trans. Autom. Sci. Eng. 2018, 15, 1178–1192.
  8. Song, H.; Rawat, D.B.; Jeschke, S.; Brecher, C. Cyber-Physical Systems: Foundations, Principles and Applications, 1st ed.; A Volume in Intelligent Data-Centric Systems; Elsevier: Waltham, MA, USA, 2017.
  9. Leng, J.; Sha, W.; Wang, B.; Zheng, P.; Zhuang, C.; Liu, Q.; Wuest, T.; Mourtzis, D.; Wang, L. Industry 5.0: Prospect and retrospect. J. Manuf. Syst. 2022, 65, 279–295.
  10. Kumar, S.; Tiwari, P.; Zymbler, M. Internet of Things is a revolutionary approach for future technology enhancement: A review. J. Big Data 2019, 6, 111.
  11. Albreem, M.A.M.; El-Saleh, A.A.; Isa, M.; Salah, W.; Jusoh, M.; Azizan, M.; Ali, A. Green internet of things (IoT): An overview. In Proceedings of the IEEE 4th International Conference on Smart Instrumentation, Measurement and Application (ICSIMA), Putrajaya, Malaysia, 28–30 November 2017; pp. 1–6.
  12. Fraga-Lamas, P.; Lopes, S.I.; Fernández-Caramés, T.M. Green IoT and Edge AI as Key Technological Enablers for a Sustainable Digital Transition towards a Smart Circular Economy: An Industry 5.0 Use Case. Sensors 2021, 21, 5745.
  13. Sisinni, E.; Saifullah, A.; Han, S.; Jennehag, U.; Gidlund, M. Industrial Internet of Things: Challenges, Opportunities, and Directions. IEEE Trans. Ind. Inform. 2018, 14, 4724–4734.
  14. Liao, Y.; de Freitas Rocha Loures, E.; Deschamps, F. Industrial Internet of Things: A Systematic Literature Review and Insights. IEEE Internet Things J. 2018, 5, 4515–4525.
  15. Wang, J.; Xu, C.; Zhang, J.; Zhong, R. Big data analytics for intelligent manufacturing systems: A review. J. Manuf. Syst. 2022, 62, 738–752.
  16. Li, C.; Chen, Y.; Shang, Y. A review of industrial big data for decision making in intelligent manufacturing. Eng. Sci. Technol. Int. J. 2022, 29, 101021.
  17. European Commission; Directorate-General for Research and Innovation; Müller, J. Enabling Technologies for Industry 5.0: Results of a Workshop with Europe’s Technology Leaders; Publications Office: Brussels, Belgium, 2020; Available online: https://data.europa.eu/doi/10.2777/082634 (accessed on 9 June 2023).
  18. Huang, S.; Wang, B.; Li, X.; Zheng, P.; Mourtzis, D.; Wang, L. Industry 5.0 and Society 5.0—Comparison, complementation and co-evolution. J. Manuf. Syst. 2022, 64, 424–428.
  19. Alves, J.; Lima, T.M.; Gaspar, P.D. Is Industry 5.0 a Human-Centred Approach? A Systematic Review. Processes 2023, 11, 193.
  20. Davenport, T.H.; Kirby, J. Just How Smart Are Smart Machines? MIT Sloan 2016, 57, 7.
  21. Broll, G.; Rukzio, E.; Paolucci, M.; Wagner, M.; Schmidt, A.; Hussmann, H. Perci: Pervasive Service Interaction with the Internet of Things. IEEE Internet Comput. 2009, 13, 74–81.
  22. do Carmo, A.P.; Vassallo, R.F.; de Queiroz, F.M.; Picoreti, R.; Fernandes, M.R.; Gomes, R.L.; Martinello, M.; Dominicini, C.K.; Guimarães, R.; Garcia, A.S.; et al. Programmable intelligent spaces for Industry 4.0: Indoor visual localization driving attocell networks. Trans. Emerg. Telecommun. Technol. 2019, 30, e3610.
  23. Steventon, A.; Wright, S. Intelligent Spaces: The Application of Pervasive ICT; Springer Science & Business Media: Berlin/Heidelberg, Germany, 2010; ISBN 978-1-84628-429-8.
  24. Baum, E.B. What Is Thought? MIT Press: Cambridge, MA, USA, 2004; ISBN 978-0-262-02548-5.
  25. Niu, L.; Lu, J.; Zhang, G. Cognition in Business Decision Support Systems. In Cognition-Driven Decision Support for Business Intelligence: Models, Techniques, Systems and Applications; Niu, L., Lu, J., Zhang, G., Eds.; Studies in Computational Intelligence; Springer: Berlin/Heidelberg, Germany, 2009; pp. 39–50.
  26. Sternberg, R.J.; Sternberg, K. Cognitive Psychology; Wadsworth/Cengage L.: Boston, MA, USA, 2011; ISBN 978-1-133-31391-5.
  27. Rüßmann, M.; Lorenz, M.; Gerbert, P.; Waldner, M.; Engel, P.; Harnisch, M.; Justus, J. Industry 4.0: The Future of Productivity and Growth in Manufacturing Industries, 2020. Available online: https://rb.gy/h4roj (accessed on 14 May 2023).
  28. Xu, X.; Lu, Y.; Vogel-Heuser, B.; Wang, L. Industry 4.0 and Industry 5.0—Inception, conception and perception. J. Manuf. Syst. 2021, 61, 530–535.
  29. Carayannis, E.G.; Morawska-Jancelewicz, J. The Futures of Europe: Society 5.0 and Industry 5.0 as Driving Forces of Future Universities. J. Knowl. Econ. 2022, 13, 3445–3471.
  30. Li, L. Reskilling and Upskilling the Future-ready Workforce for Industry 4.0 and Beyond. Inf. Syst. Front. 2022.
  31. Maddikunta, P.K.R.; Pham, Q.V.; Prabadevi, B.; Deepa, N.; Dev, K.; Gadekallu, T.R.; Ruby, R.; Liyanage, M. Industry 5.0: A survey on enabling technologies and potential applications. J. Ind. Inf. Integr. 2022, 26, 100257.
  32. Özdemir, V.; Hekim, N. Birth of Industry 5.0: Making Sense of Big Data with Artificial Intelligence, “The Internet of Things” and Next-Generation Technology Policy. OMICS J. Integr. Biol. 2018, 22, 65–76.
  33. Fraga-Lamas, P.; Barros, D.; Lopes, S.I.; Fernández-Caramés, T.M. Mist and Edge Computing Cyber-Physical Human-Centered Systems for Industry 5.0: A Cost-Effective IoT Thermal Imaging Safety System. Sensors 2022, 22, 8500.
  34. Dechouniotis, D.; Athanasopoulos, N.; Leivadeas, A.; Mitton, N.; Jungers, R.; Papavassiliou, S. Edge Computing Resource Allocation for Dynamic Networks: The DRUID-NET Vision and Perspective. Sensors 2020, 20, 2191.
  35. Qiu, T.; Chi, J.; Zhou, X.; Ning, Z.; Atiquzzaman, M.; Wu, D.O. Edge Computing in Industrial Internet of Things: Architecture, Advances and Challenges. IEEE Commun. Surv. Tutor. 2020, 22, 2462–2488.
  36. Mao, Y.; You, C.; Zhang, J.; Huang, K.; Letaief, K.B. A Survey on Mobile Edge Computing: The Communication Perspective. IEEE Commun. Surv. Tutor. 2017, 19, 2322–2358.
  37. Satyanarayanan, M. The Emergence of Edge Computing. Computer 2017, 50, 30–39.
  38. Omoniwa, B.; Hussain, R.; Javed, M.A.; Bouk, S.H.; Malik, S.A. Fog/Edge Computing-Based IoT (FECIoT): Architecture, Applications, and Research Issues. IEEE Internet Things J. 2019, 6, 4118–4149.
  39. Gupta, S. An edge-computing based Industrial Gateway for Industry 4.0 using ARM TrustZone technology. J. Ind. Inf. Integr. 2023, 33, 100441.
  40. Bonomi, F.; Milito, R.; Natarajan, P.; Zhu, J. Fog Computing: A Platform for Internet of Things and Analytics. In Big Data and Internet of Things: A Roadmap for Smart Environments; Bessis, N., Dobre, C., Eds.; Studies in Computational Intelligence; Springer International Publishing: Cham, Switzerland, 2014; pp. 169–186.
  41. Yi, S.; Li, C.; Li, Q. A Survey of Fog Computing: Concepts, Applications and Issues. In Proceedings of the Mobidata ’15: Proceedings of the 2015 Workshop on Mobile Big Data, New York, NY, USA, 21 June 2015; pp. 37–42.
  42. Kannisto, P.; Hästbacka, D.; Gutiérrez, T.; Suominen, O.; Vilkko, M.; Craamer, P. Plant-wide interoperability and decoupled, data-driven process control with message bus communication. J. Ind. Inf. Integr. 2022, 26, 100253.
  43. Deshpande, S.; Jogdand, R. Development of IoT Middleware Broker Communication Architecture for Industrial Automation with Focus on Future Pandemic Possibilities: Industry 5.0. In Proceedings of the Emerging Technologies in Data Mining and Information Security; Advances in Intelligent Systems and Computing; Springer: Singapore, 2023; pp. 47–58.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Engineering, Electrical & Electronic
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Wagner Augusto Aranda Cotta
,
Sérgio Ivan Lopes
,
Raquel Frizera Vassallo
View Times: 345
Revisions: 2 times (View History)
Update Date: 02 Nov 2023
Table of Contents
    1000/1000
    Hot Most Recent
    ScholarVision Creations
    Feedback