Abstract
Intelligent manufacturing ecosystem and using Industry 4.0 strategic plan as a one-stop-shop services promise increasing flexibility in the digital manufacturing process, mass customization of manufacturing integration, better quality, beneficially, and improved productivity. This chapter demonstrates state-of-art technologies that include several applications of MTConnect standard technology and the digital twin, and an affordable digital twin solution for small and medium-sized enterprises (SMEs) is proposed based on [1, 2]. The chapter points to current and future challenges, limitations, and necessary changes in a digital twin.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Al-Dubaee, S., Juneja, J.: An affordable portable IoT development kit and I4.0 platform an internal report of Advanced Forming Research Centre (AFRC), DMEM, Strathclyde University, Scotland (2020)
Lu, Y., Liu, C., Wang, K.-K., Huang, H., Xu, X.: Digital Twin-driven smart manufacturing: connotation, reference model, applications and research issues. Robot. Comput. Integr. Manuf. 61, 1–14 (2020)
Zhong, R.Y., Xu, X., Klotz, E., Newman, S.T.: Intelligent manufacturing in the context of industry 4.0: a review. Engineering 3(5), 616–630 (2017)
Fisher, R., Shao, G.: Testing of the MTConnect-OPC UA Compnation Specification. Erie, PA, USA (2019)
Bamunuarachchi, D., Banerjee, A., Jayaraman, P.P., Georgakopoulos, D.: Cyber Twins Supporting Industry 4.0 Application Development. Chiang Mai, Thailand, ACM, New York, USA (2020)
Xing, K., Liu, X., Liu, Z., Mayer, J., Achiche, S. Low Cost Precsion Monitoring System of machine Tools for SMEs. Canada (2021)
Shao, G., Kibira, D.: Digital Manufacturing: Requirements and Challenges for Implementing Digital Surrogates. Washington DC, (2018)
Zhong, L., Yongliang, L., Fei, T., Hu, L.B., Lei, R., Xuesong, Z., Hua, G., Ying, C., Anrui, H., Ying, C., Anrui, H., Yongkui, L.: Cloud manufacturing: a new manufacturing paradigm. Enterp. Inf. Syst. 8(2), 167–187 (2012)
Tao, F., Cheng, J., Qi, Q., Zhang, M., Sui, F.: Digital twin-driven product design, manufacturing and service with big data. J. Manuf. Syst. 157–169 (2018)
Liu, M., Ma, J., Lin, L., Ge, M., Wang, Q., Liu, C.: Intelligent assembly system for mechanical products and key technology based on internet of things. J. Intell. Manuf. 28(02), 271–299 (2017)
Parwekar, P.: From internet of things towards cloud of things. In: 2nd International Conference on Computer and Communication Technology (ICCCT) (2011)
Sauter, T.: The three generations of field-level networks—evolution and compatibility issues. IEEE Trans. Industr. Electron. 57(11), 3585–3595 (2010)
Bertoluzzo, M., Buja, G., Vitturi, S.: Ethernet networks for factory automation. In: IEEE International Symposium on Industrial Electronics (2002)
Vitturi, S., Sauter, T., Pang, Z.: Real-time networks and protocols for factory automation and process control systems [scanning the issue] 107(6), 939–943 (2019)
Immerman, G.: The Machine Builders’ Guide to Remote Machine Monitoring,” machinemetrics. https://www.machinemetrics.com/ (2018). Accessed 04 June 2021
Wang, L., Orban, P., Cunningham, A., Lang, S.: Remote real-time CNC machining for web-based manufacturing. Robot. Comput. Integr. Manuf. 20, 563–571 (2004)
Newman, D., Parto, M., Saleeby, K., Kurfess, T., Dugenske, A.: Development of a Digital Architecture for Distributed CNC Machine Health Monitoring, 1–19 (2019)
Grieves, M., Vickers, J.: Digital twin: mitigating unpredictable, undesirable emergent behavior in complex systems. In: Transdisciplinary Perspectives on Complex Systems, pp. 85–113. Springer International Publishing Switzerland, NASA MSFC, Huntsville, AL, USA (2017)
Pokhrel, A., Katta, V., Palacios, R.C.: Digital Twin for Cybersecurity Incident Prediction: A Multivocal Literature Review. Republic of Korea, Seoul (2020)
Huang, S., Zhou, C.-J., Yang, S.-H.: Cyber-physical system security for networked industrial processes. Int. J. Autom. Comput. 12(6), 567–578 (2015)
Dong, P., Han, Y., Guo, X., Xie, F.: A systematic review of studies on cyber physical system security. Int. J. Secur. Appli. 09(01), 155–164 (2015)
Tao, F., Zhang, M., Nee, A.Y.C.: Digial Twin Driven Smart Manufacturing, 1st edn. Academic Press is an imprint of Elsevier, Chennai, India (2019)
Hribernik, K.A., Rabe, L., Schumacher, J., Thoben, K.D.: Int. J. Prod. Lifecycle Manag. 4(1), 367–379 (2006)
Grieves M.W.: Virtually intelligent product systems:digital and physical twins. In: Complex Systems Enginnering:Theroy and Practice, Florida Institute of Technology, Melbourne, FL USA, American Institute of Aeronautics and Astronautics, pp 175–200 (2019)
Hehenberger, P., Bradley, D.: Mechatronic Futures Challenges and Solutions for Mechatronic Systems and their Designers, pp. 1–273. Springer International, AG Switzerland (2016)
Glaessgen, E.H., Stargel, D.S.: The Digital Twin Paradigm for Future NASA and U.S. Air Force Vehicles. Honolulu, United States (2012)
Jaensch, F., Csiszar, A., Scheifele, C., Verl, A.: Digital twins of manufacturing systems as a base for machine learning. In: Mechatronics and Machine Vision in Practice (M2VIP) (2018)
Soderberg, R., Warmefjord, K., Carlson, J.S., Lindkvist, L.: Toward a Digital Twin for real-time geometry assurance in individualized production. CIRP Ann. Manuf. Technol. 66(1), 137–140 (2017)
Xia, K., Sacco, C., Kirkpatrick, M., Saidy, C., Nguyen, L., Kircaliali, A., Harik, R.: A digital twin to train deep reinforcement learning agent for smart manufacturing plants: environment, interfaces and intelligence. J. Manuf. Syst. 58, 210–230 (2021)
Min, Q., Lu, Y., Liu, Z., Su, C., Wang, B.: Machine learning based digital twin framework for production optimization in petrochemical industry. Int. J. Inf. Manage. 49, 502–519 (2019)
Yildiz, E., Moller, C., Bilberg, A.: Virtual factory: digital twin based integrated factory simulations. In: Virtual Factory: Digital Twin Based Integrated Factory Simulations. Denmark (2020)
Qi, Q., Tao, F.: Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison. Digit. Object Identifier 6, 3585–3593 (2018)
Rosen, R., Wichert, G.V., Lo, G., Bettenhausen, K.D.: About the importance of autonomy and digital twins for the future of manufacturing. In IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd (2015)
Zhoua, G., Zhanga, C., Li, Z., Ding, K., Wang, C.: Knowledge-driven digital twin manufacturing cell towards intelligent manufacturing. Int. J. Prod. Res. 58, 1034–1051 (2020)
Aheleroff, S., Xu, X., Zhong, R.Y., Lu, Y.: Digital twin as a service (DTaaS) in industry 4.0: an architecture reference model. Adv. Eng. Inform. 47, 1–15 (2021)
European Commission: Internal Market, Industry, Entrepreneurship and SMEs. European Commission, 01 Jan 2020. https://ec.europa.eu/growth/industry_en (2020). Accessed 14 June 2021
Edstrom, D.: The History of MTConnect®. http://photonsandelectrons.blogspot.com/2011/04/history-of-mtconnect.html (2011). Accessed 22 Feb 2021
Navas, C.F., Yepesb, A.E., Abolghasem, S., Barbieri, G.: MTConnect-based decision support system for local machine tool monitoring. In: International Conference on Industry 4.0 and Smart Manufacturing, pp. 1–10 (2019)
Korn, D.: Remote honing analytics with MTconnect-compliant machines. Mach Technol Brief 89(10), 28–30 (2017)
Venkatesh, S., Ly, S., Manning, M., Michaloski, J., Proctor, F.: Automating Asset Knowledge With Mtconnect. U.S. Department of Commerce, vol. 03, p. 20. National Institute of Standards and Technology (2016)
Matt, D.T., Modrák, V., Zsifkovits, H.: Industry 4.0 for SMEs Challenges, Opportunities and Requirements, Gewerbestrasse 11, 6330. Springer International Publishing, Cham, Switzerland (2020)
Chalmers, R.: High-speed machining means more than quick spindles. High-Speed Mach 131(10), 34–36 (2019)
Cui, Y., Kara, S., Chan, K.C.: Large scale MTConnect data collection. IEEE Xplore 06 (2021)
Albert, M.: Metal working’s leading information resource, 01 April 2014. https://www.mmsonline.com/ (2014). Accessed 26 March 2021
Jain, S., Shao, G., Shin, S.-J.: Manufacturing data analytics using a virtual factory representation. Int. J. Prod. Res. 55, 17 (2017)
Wang, L., Törngren, M., Onori, M.: Current status and advancement of cyber-physical systems in manufacturing. J. Manuf. Syst. 37, 517–527 (2015)
Boyes, H., Hallaq, B., Cunningham, J., Watson, T.: The industrial internet of things (IIoT): an analysis framework. Comput. Ind. 101, 1–12 (2018)
Cheng, G.J., Liu, L.T., Qiang, X.J., Liu, Y.: Industry 4.0 Development and Application of Intelligent Manufacturing (2016)
Alguliyev, R., Imamverdiyev, Y., Sukhostat, L.: Cyber-physical systems and their security issues. Comput. Ind. 100, 212–223 (2018)
Carvalho, N., Chaim, O., Cazarinia, E., Gerolamo, M.: Manufacturing in the fourth industrial revolution: A positive prospect in sustainable manufacturing (2018)
Rassam, M.A., Zainal, A., Maa, M.A.: An adaptive and efficient dimension reduction model for multivariate wireless sensor networks applications. Appl. Soft Comput. 13(4), 1978–1996 (2013)
Fantini, P., Tavola, G., Taisch, M., Barbosa, J., Leitao, P., Liu, Y., Sayed, M.S., Lohse, N.: Exploring the integration of the human as a flexibility factor in CPS enabled manufacturing environments: methodology and results. IEEE, pp 1–6 (2016)
Liu, Z., Wang, Z., Ren, Y., Feng, Q., Fan, D., Zuo, Z.: A City medical resources distribution optimization platform based on Cyber-Physical Systems(CPS),” in IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control, pp 69-73 (2018)
Kale, G., Patil, A.: Prototype Architecture to Control Building Energy Usage Using PLC and CPS (2018)
Möller, D.P., Vakilzadian, H.: Cyber-Physical Systems in Smart Transportation (2016)
de Araujo, P.R.M., Lins, R.G.: Cloud-based approach for automatic CNC workpiece origin localization based on image analysis☆. Robot. Comput. Integr. Manuf. 68, 1–16 (2021)
Konga, X.T., Zhong, R.Y., Zhao, Z., Shao, S., Li, M., Lin, P., Chen, Y., Wu, W., Shen, L., Yu, Y., Huang, G.Q.: Cyber physical ecommerce logistics system: an implementation case in Hong Kong. Comput. Ind. Eng. 139, 1–15 (2020)
Parris, C., P.S.V.P.: Chief Technology Officer, GE Digital: What is the industrial internet of things (IIoT)? https://www.ge.com/digital/blog/what-industrial-internet-things-iiot. (2019). Accessed 17 May 2021
Omron: Advanced sensing solutions for cost-effective machine building. https://assets.omron.com/m/44e2ef9d76877d4c/original/Sensor-Machine-Builders-Whitepaper.pdf (2019). Accessed 21 Feb 2021
Klaess, J.: Increase the productivity, quality and efficiency of your operations by implementing Industry 4.0 technologies with low investments and 10x faster than normal. www.konitech.com.br/a-convergencia-de-it-e-ot (2019). Accessed 17 May 2021
Desrosiers, N.: Intelligent monitoring with MTConnect. Moldmaking Technol. Mag. 21(7), 48 (2018)
Zhang, J., Deng, T., Jiang, H., Chen, H., Qin, S., Ding, G.: Bi-level dynamic scheduling architecture based on service unit digital twin agents. J. Manuf. Syst. 60, 59–79 (2021)
Beimborn, D., Miletzki, T., Wenzel, S.: Platform as a service (PaaS). Bus. Inf. Syst. Eng. 3(6), 381–384 (2011)
Palos-Sanche, P.R., Arenas-Marquez, F., Aguayo-Camacho, M.: Cloud Computing (SaaS) Adoption as a Strategic Technology:Results of an Empirical Study. Hindawi, 1–20 (2017)
Ferguson, P., Huston, G.: What is a VPN? 1(01), 01–22 (1998)
Chirayil, A. Survey on Anomaly Detection in Wireless Sensor Networks (WSNs), 99p (2019)
Shao, G., Helu, M.: Framework for a digital twin in manufacturing: scope and requirements. Manuf. Lett. 24, 105–107 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
M. Sharadah, F., Al-Dubaee, S., Weir, G. (2022). MTConnect and Digital Twin Applications and Future Perspectives. In: Hassanien, A.E., Darwish, A., Snasel, V. (eds) Digital Twins for Digital Transformation: Innovation in Industry. Studies in Systems, Decision and Control, vol 423. Springer, Cham. https://doi.org/10.1007/978-3-030-96802-1_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-96802-1_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-96801-4
Online ISBN: 978-3-030-96802-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)