Abstract
The ability to connect a growing range of technologies, such as sensors, Internet of (Industrial) Things, cloud computing, Big Data analytics, AI, mobile devices, and augmented/virtual reality, is helping to take manufacturing to new levels of “smartness.” Such technologies have the opportunity to transform, automate, and bring intelligence to manufacturing processes and support the next manufacturing era. In this chapter, we describe the manufacturing context; emerging concepts, such as Industry 4.0; and technologies that are driving change and innovation within the manufacturing industry.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Al-Abassi, A., Karimipour, H., HaddadPajouh, H., Dehghantanha, A., & Parizi, R. M. (2020). Industrial big data analytics: Challenges and opportunities. In K. K. Choo & A. Dehghantanha (Eds.), Handbook of big data privacy. Cham: Springer.
Alcácer, V., & Cruz-Machado, V. (2019). Scanning the Industry 4.0: A literature review on technologies for manufacturing systems. Engineering Science and Technology, an International Journal, 22(3), 899–919.
Bralla, J. G. (2007). Handbook of manufacturing processes – How products, components and materials are made. New York: Industrial Press.
Cai, Y., Starly, B., Cohen, P., & Lee, Y. S. (2017). Sensor data and information fusion to construct digital-twins virtual machine tools for cyber-physical manufacturing. Procedia Manufacturing, 10, 1031–1042.
Caudell, T. P., & Mizell, D. W. (1992). Augmented reality: An application of heads-up display technology to manual manufacturing processes. In Hawaii International Conference on System Sciences (pp. 659–669).
Chu, L. P. (2016). Data science for modern manufacturing: Global trends: Big data analytics for the industrial Internet of Things. O’Reilly Media. ISBN: 1491958960.
Dahotre, N. B., & Harimkar, S. P. (2008). Manufacturing processes: An overview. Laser Fabrication and Machining of Materials, 69–96.
Dominguez-Caballero, J., Stammers, J., & Moore, J. (2019). Development and testing of a combined machine and process health monitoring system. Procedia CIRP, 86, 20–25.
Duro, J. A., Padget, J. A., Bowen, C. R., Kim, H. A., & Nassehi, A. (2016). Multi-sensor data fusion framework for CNC machining monitoring. Mechanical Systems and Signal Processing, 66, 505–520.
Eyre, J., Hyde, S., Walker, D., Ojo, S., Hayes, O., Hartley, R., Scott, R., & Bray, J. (2020). Untangling the requirements of a Digital Twin. Advanced Manufacturing Research Centre. Technical Report. Available online: https://www.amrc.co.uk/files/document/406/1605271035_1604658922_AMRC_Digital_Twin_AW.pdf
Fernández, D. S., Jackson, M., Crawforth, P., Fox, K., & Wynne, B. P. (2020). Using machining force feedback to quantify grain size in beta titanium. Materialia, 13, 100856.
Fujishima, M., Ohno, K., Nishikawa, S., Nishimura, K., Sakamoto, M., & Kawai, K. (2016). Study of sensing technologies for machine tools. CIRP Journal of Manufacturing Science and Technology, 14, 71–75.
Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144.
Gao, R. X., Wang, L., Helu, M., & Teti, R. (2020). Big data analytics for smart factories of the future. CIRP Annals, 69(2), 668–692.
Hariri, R. H., Fredericks, E. M., & Bowers, K. M. (2019). Uncertainty in big data analytics: Survey, opportunities, and challenges. Journal of Big Data, 6, 44.
Heeley, A. D., Hobbs, M. J., Laalej, H., & Willmott, J. R. (2018). Miniature uncooled and unchopped fiber optic infrared thermometer for application to cutting tool temperature measurement. Sensors, 18(10), 3188.
Hentout, A., Aouache, M., Maoudj, A., & Akli, I. (2019). Human–robot interaction in industrial collaborative robotics: A literature review of the decade 2008–2017. Advanced Robotics, 33(15–16), 764–799.
Hughes, R. (2018). Virtual simulation of new Boeing facility based in Sheffield. Advanced Manufacturing Research Centre. Technical Report. Available online: https://www.amrc.co.uk/files/document/241/1542814525_AMRC_BOEING_case_study.pdf
Khan, W. Z., Rehman, M. H., Zangoti, H. M., Afzal, M. K., Armi, N., & Salah, K. (2020). Industrial Internet of things: Recent advances, enabling technologies and open challenges. Computers & Electrical Engineering, 81, 106522.
Kritzinger, W., Karner, M., Traar, G., Henjes, J., & Sihn, W. (2018). Digital twin in manufacturing: A categorical literature review and classification. IFAC-PapersOnLine, 51(11), 1016–1022.
Kusiak, A. (2018). Smart manufacturing. International Journal of Production Research, 56(1–2), 508–517.
Lee, G., Kim, M., Quan, Y., Kim, M., Kim, T. J. Y., Yoon, H., Min, S., Kim, D., Mun, J., Oh, J. W., Choi, I. G., Kim, C., Chu, W., Yang, J., Bhandari, B., Lee, C., Ihn, J., & Ahn, S. (2018). Machine health management in smart factory: A review. Journal of Mechanical Science and Technology, 32(3), 987–1009.
Li, B. H., Hou, B. C., Yu, W. T., Lu, X. B., & Yang, C. W. (2017). Applications of artificial intelligence in intelligent manufacturing: A review. Frontiers of Information Technology and Electronic Engineering. Zhejiang University.
Lockwood, A. J., Hill, G., Moldoveanu, M., Coles, R., & Scott, R. (2018). Digitalisation of legacy machine tools. AMRC Technical Report. Available online: https://www.amrc.co.uk/files/document/239/1542365809_WHITE_PAPER_LEGACY_AW.pdf
Lu, Y., Witherell, P., & Jones, A. (2020). Standard connections for IIoT empowered smart manufacturing. Manufacturing Letters, 26, 17–20.
Maier, W., Möhring, H. C., & Werkle, K. (2018). Tools 4.0–Intelligence starts on the cutting edge. Procedia Manufacturing, 24, 299–304.
Mourtzis, D. (2020). Simulation in the design and operation of manufacturing systems: State of the art and new trends. International Journal of Production Research, 58(7), 1927–1949.
Oussous, A., Benjelloun, F. Z., Lahcen, A. A., & Belfkih, S. (2018). Big data technologies: A survey. Journal of King Saud University-Computer and Information Sciences, 30(4), 431–448.
Patel, P., Ali, M. I., & Sheth, A. (2018). From raw data to smart manufacturing: AI and semantic web of things for industry 4.0. IEEE Intelligent Systems, 33(4), 79–86.
Qu, Y. J., Ming, X. G., Liu, Z. W., Zhang, X. Y., & Hou, Z. T. (2019). Smart manufacturing systems: State of the art and future trends. The International Journal of Advanced Manufacturing Technology, 103(9–12), 3751–3768.
Schmitt, J., Bönig, J., Borggräfe, T., Beitinger, G., & Deuse, J. (2020). Predictive model-based quality inspection using machine learning and edge cloud computing. Advanced Engineering Informatics, 45, 101101.
Syberfeldt, A., Danielsson, O., & Gustavsson, P. (2017). Augmented reality smart glasses in the smart factory: Product evaluation guidelines and review of available products. IEEE Access, 5, 9118–9130.
Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018). Data-driven smart manufacturing. Journal of Manufacturing Systems, 48, 157–169.
Wu, D., Weiss, B. A., Kurfess, T., Wang, L., & Davis, J. (2018). Introduction to the special issue on smart manufacturing. Journal of Manufacturing Systems, 48, 1–2.
Xia, C., Pan, Z., Polden, J., Li, H., Xu, Y., Chen, S., & Zhang, Y. (2020). A review on wire arc additive manufacturing: Monitoring, control and a framework of automated system. Journal of Manufacturing Systems, 57, 31–45.
Xu, L. D., Xu, E. L., & Li, L. (2018). Industry 4.0: State of the art and future trends. International Journal of Production Research, 56(8), 2941–2962.
Yang, Z., Zhang, P., & Chen, L. (2016). RFID-enabled indoor positioning method for a real-time manufacturing execution system using OS-ELM. Neurocomputing, 174, 121–133.
Zhang, W., Cai, W., Min, J., Fleischer, J., Ehrmann, C., Prinz, C., & Kreimeier, D. (2020). 5G and AI technology application in the AMTC learning factory. Procedia Manufacturing, 45, 66–71.
Zheng, P., Sang, Z., Zhong, R. Y., Liu, Y., Liu, C., Mubarok, K., Xu, X., et al. (2018). Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives. Frontiers of Mechanical Engineering, 13(2), 137–150.
Zonta, T., da Costa, C. A., da Rosa Righi, R., de Lima, M. J., da Trindade, E. S., & Li, G. P. (2020). Predictive maintenance in the Industry 4.0: A systematic literature review. Computers & Industrial Engineering, 106889.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Clough, P.D., Stammers, J. (2021). Smart Manufacturing. In: Jain, S., Murugesan, S. (eds) Smart Connected World. Springer, Cham. https://doi.org/10.1007/978-3-030-76387-9_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-76387-9_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-76386-2
Online ISBN: 978-3-030-76387-9
eBook Packages: Computer ScienceComputer Science (R0)