Product: Management and Development
Product: Management and Development
Review Article

Digital twin: a concept in evolution

Luiz Fernando Cardoso dos Santos Durão, Eduardo de Senzi Zancul, Klaus Schützer

Downloads: 1
Views: 404


Digital Twin is defined as a realistic digital model of an object's physical state, representing its interaction with the environment in the real world. The research on Digital Twin has been advancing intensively in recent years. As a result of an emerging and broad research topic, various interpretations and Digital Twin applications have been developed. In this scenario, there is an opportunity to research the Digital Twin types and understand the concept evolvement. This paper provides an overview of the Digital Twin concept, classifies the existing body of literature, and discusses the Digital Twin evolution. Therefore, this research applies a combination of methods, including bibliometrics, natural language processing, and content analysis. The results show an expansion of Digital Twin's role from an enabler of cyber-physical systems to a product lifecycle data integration and processing platform.


digital twin, advanced manufacturing, natural language analysis, Industry 4.0.


Alam, K. M., & El Saddik, A. (2017). C2PS: A digital twin architecture reference model for the cloud-based cyber-physical systems. IEEE Access: Practical Innovations, Open Solutions, 5, 2050-2062.

Barbosa, S., Silva, F. P., Rafael, L., & Otto, R. B. (2018). Virtual assistant to real time training on industrial environment. Amsterdam: IOS Press.

Bazilevs, Y., Deng, X., Korobenko, A., Di Scalea, F. L., Todd, M. D., & Taylor, S. G. (2015). Isogeometric fatigue damage prediction in large-scale composite structures driven by dynamic sensor data. Journal of Applied Mechanics, Transactions ASME, 82(9), 091008.

Biffl, S., Gerhard, D., & Lüder, A. (2017). Multi-disciplinary engineering for cyber-physical production systems: Data models and software solutions for handling complex engineering projects. Switzerland: Springer Nature.

Bigoni, C., & Hesthaven, J. S. (2020). Simulation-based anomaly detection and damage localization: an application to structural health monitoring. Computer Methods in Applied Mechanics and Engineering, 363, 112896.

Brenner, B., & Hummel, V. (2017). Digital twin as enabler for an innovative digital shopfloor management system in the ESB logistics learning factory at reutlingen - University. Procedia Manufacturing, 9, 198-205.

Burghardt, A., Szybicki, D., Gierlak, P., Kurc, K., Pietruś, P., & Cygan, R. (2020). Programming of industrial robots using virtual reality and digital twins. Applied Sciences (Switzerland), 10(2), 486.

Canedo, A. (2016). Industrial IoT lifecycle via digital twins. In Proceedings of the Eleventh IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis (CODES '16) (Article 29, pp. 1). New York, USA: Association for Computing Machinery.

Carvalho, M. M., Fleury, A., & Lopes, A. P. (2013). An overview of the literature on technology roadmapping (TRM): contributions and trends. Technological Forecasting and Social Change, 80(7), 1418-1437.

Cheng, Y., Zhang, Y., Ji, P., Xu, W., Zhou, Z., & Tao, F. (2018). Cyber-physical integration for moving digital factories forward towards smart manufacturing : a survey. International Journal of Advanced Manufacturing Technology, 97(1-4), 1209-1221.

Desai, N., Ananya, S. K., Bajaj, L., & Periwal, A. (2020). Cyber-physical systems and digital twins. Rev, 2019(80), 74-80.

Ding, K., Chan, F. T. S., Zhang, X., Zhou, G., & Zhang, F. (2019). Defining a digital twin-based cyber-physical production system for autonomous manufacturing in smart shop floors. International Journal of Production Research, 57(20), 6315-6334.

Durão, L. F. C. S., Grotti, M. V. F., Maceta, P. R. M., Zancul, E. de S., Berssaneti, F. T., & Carvalho, M. M. (2017). A review of the soft side in project management: concept, trends and challenges. GEPROS: Revista Gestão da Produção Operações e Sistemas, 12(2), 157-176.

Duriau, V., Reger, R., & Pfarrer, M. D. (2007). A content analysis of the content analysis literature in organization studies research themes, data sources, and methodological refinements. Organizational Research Methods, 10(1), 5-34.

El Saddik, A. (2018). Digital twins: the convergence of multimedia technologies. IEEE MultiMedia, 25(2), 87-92.

Erdős, G., Paniti, I., & Tipary, B. (2020). Transformation of robotic workcells to digital twins. CIRP Annals, 69(1), 149-152.

Erkoyuncu, J. A., del Amo, I. F., Ariansyah, D., Bulka, D., Vrabič, R., & Roy, R. (2020). A design framework for adaptive digital twins. CIRP Annals, 69(1), 145-148.

Glaessgen, E. H., & Stargel, D. S. (2012). The digital twin paradigm for future NASA and U.S. Air force vehicles. In 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference (pp. 1-14). Reston: American Institute of Aeronautics and Astronautic.

Golizadeh Akhlaghi, Y., Badiei, A., Zhao, X., Aslansefat, K., Xiao, X., Shittu, S., & Ma, X. (2020). A constraint multi-objective evolutionary optimization of a state-of-the-art dew point cooler using digital twins. Energy Conversion and Management, 211, 112772.

Greif, T., Stein, N., & Flath, C. M. (2020). Peeking into the void: digital twins for construction site logistics. Computers in Industry, 121, 103264.

Haag, S., & Anderl, R. (2018). Digital twin: proof of concept. Manufacturing Letters, 15, 64-66.

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: data mining, inference, and prediction (2nd ed.). ew York: Springer.

Havard, V., Jeanne, B., Lacomblez, M., & Baudry, D. (2019). Digital twin and virtual reality: a co-simulation environment for design and assessment of industrial workstations. Production & Manufacturing Research, 7(1), 472-489.

He, B., & Bai, K. J. (2021). Digital twin-based sustainable intelligent manufacturing: a review. Advances in Manufacturing, 9(1), 1

Ivanov, D. (2020). Predicting the impacts of epidemic outbreaks on global supply chains: a simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transportation Research Part E, Logistics and Transportation Review, 136, 101922.

Jones, D., Snider, C., Nassehi, A., Yon, J., & Hicks, B. (2020). Characterising the digital twin: a systematic literature review. CIRP Journal of Manufacturing Science and Technology, 29, 36-52.

Kannan, K., & Arunachalam, N. (2019). A digital twin for grinding wheel: an information sharing platform for sustainable grinding process. Journal of Manufacturing Science and Engineering, 141(2), 021015.

Kaur, M. J., Mishra, V. P., & Maheshwari, P. (2020). The convergence of digital Twin, IoT, and machine learning: transforming data into action. In M. Farsi, A. Daneshkhah, A. Hosseinian-Far & H. Jahankhani (Eds.), Digital Twin technologies and smart cities. Cham: Springer International Publishing.

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.

Lim, K. Y. H., Zheng, P., & Chen, C. H. (2020). A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives. Journal of Intelligent Manufacturing, 31(6), 1313-1337.

Liu, J., Zhou, H., Tian, G., Liu, X., & Jing, X. (2019a). Digital twin-based process reuse and evaluation approach for smart process planning. International Journal of Advanced Manufacturing Technology, 100(5-8), 1619-1634.

Liu, Q., Zhang, H., Leng, J., & Chen, X. (2019b). Digital twin-driven rapid individualised designing of automated flow-shop manufacturing system. International Journal of Production Research, 57(12), 3903-3919.

Lu, Y., Min, Q., Liu, Z., & Wang, Y. (2019). An IoT-enabled simulation approach for process planning and analysis : a case from engine re- manufacturing industry. International Journal of Computer Integrated Manufacturing, 32(4–5), 413-429.

Lu, Y., & Xu, X. (2018). Resource virtualization: a core technology for developing cyber-physical production systems. Journal of Manufacturing Systems, 47, 128-140.

Ma, X., Tao, F., Zhang, M., Wang, T., & Zuo, Y. (2019). Digital twin enhanced human-machine interaction in product lifecycle. Procedia CIRP, 83, 789-793.

Moyne, J., & Iskandar, J. (2017). Big data analytics for smart manufacturing: case studies in semiconductor manufacturing. Processes, 5(3), 39.

Negri, E., Fumagalli, L., & Macchi, M. (2017). A review of the roles of digital twin in CPS-based production systems. Procedia Manufacturing, 11, 939-948.

Park, K. T., Lee, J., Kim, H.-J., & Noh, S. D. (2020). Digital twin-based cyber physical production system architectural framework for personalized production. International Journal of Advanced Manufacturing Technology, 106(5-6), 1787-1810.

Park, K. T., Nam, Y. W., Lee, H. S., Im, S. J., Noh, S. D., Son, J. Y., & Kim, H. (2019). Design and implementation of a digital twin application for a connected micro smart factory. International Journal of Computer Integrated Manufacturing, 32(6), 596-614.

Qi, Q., & Tao, F. (2018). Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison. IEEE Access : Practical Innovations, Open Solutions, 6, 3585-3593.

Qiao, Q., Wang, J., Ye, L., & Gao, R. X. (2019). Digital twin for machining tool condition prediction. Procedia CIRP, 81, 1388-1393.

Raj, P., & Surianarayanan, C. (2020). Digital twin: the industry use cases. Advances in Computers, 117(1), 285-320.

Rajesh, P. K., Manikandan, N., Ramshankar, C. S., Vishwanathan, T., & Sathishkumar, C. (2019). Digital twin of an automotive brake pad for predictive maintenance. Procedia Computer Science, 165, 18-24.

Raza, M., Kumar, P. M., Hung, D. V., Davis, W., Nguyen, H., & Trestian, R. (2020). A digital twin framework for industry 4.0 enabling next-gen manufacturing. In ICITM 2020 - 2020 9th International Conference on Industrial Technology and Management (pp. 73-77). New York: IEEE.

Rodič, B. (2017). Industry 4.0 and the new simulation modelling paradigm. Organizacija, 50(3), 193-207.

Romero, D., Mattsson, S., & Fast-berglund, Å. (2018). Advances in production management systems: smart manufacturing for industry 4.0. In IFIP WG 5.7 International Conference, APMS 2018 (Vol. 536). Switzerland: Springer Nature.

Rosen, R., Von Wichert, G., Lo, G., & Bettenhausen, K. D. (2015). About the importance of autonomy and digital twins for the future of manufacturing. IFAC-PapersOnLine, 28(3), 567-572.

Roy, R. B., Mishra, D., Pal, S. K., Chakravarty, T., Panda, S., Chandra, M. G., Pal, A., Misra, P., Chakravarty, D., & Misra, S. (2020). Digital twin: current scenario and a case study on a manufacturing process. International Journal of Advanced Manufacturing Technology, 107(9–10), 3691-3714.

Schilling, K., Stanetzky, D., & Brecher, C. (2019). A mixed reality application for linked data in engineering and production. In MuC'19: Proceedings of Mensch und Computer 2019 (pp. 673-676). ACM.

Schleich, B., Anwer, N., Mathieu, L., & Wartzack, S. (2017). Shaping the digital twin for design and production engineering. CIRP Annals - Manufacturing Technology, 66(1), 141-144.

Schluse, M., Priggemeyer, M., Atorf, L., & Rossmann, J. (2018). Experimentable digital twins-streamlining simulation-based systems engineering for industry 4.0. IEEE Transactions on Industrial Informatics, 14(4), 1722-1731.

Schmetz, A., Lee, T. H., Hoeren, M., Berger, M., Ehret, S., Zontar, D., Min, S. H., Ahn, S. H., & Brecher, C. (2020). Evaluation of Industry 4.0 data formats for digital twin of optical components. International Journal of Precision Engineering and Manufacturing, 7(3), 573-584.

Schuh, G., Jussen, P., & Harland, T. (2018). The digital shadow of services: a reference model for comprehensive data collection in MRO services of machine manufacturers. Procedia CIRP, 73, 271-277.

Shaikh, F. K., Zeadally, S., & Exposito, E. (2017). Enabling technologies for social internet of things. IEEE Systems Journal, 11(2), 983-994.

Siegert, J., Schlegel, T., Groß, E., & Bauernhansl, T. (2017). Standardized coordinate system for factory and production planning. Procedia Manufacturing, 9(711), 127-134.

Stark, R., & Damerau, T. (2019). Digital twin. In S. Chatti & T. Tolio (Eds.), CIRP Encyclopedia of Production Engineering. Berlin: Springer.

Stark, R., Fresemann, C., & Lindow, K. (2019). Development and operation of Digital Twins for technical systems and services. CIRP Annals, 68(1), 129-132.

Stavropoulos, P., Papacharalampopoulos, A., & Athanasopoulou, L. (2020). A molecular dynamics based digital twin for ultrafast laser material removal processes. International Journal of Advanced Manufacturing Technology, 108(1-2), 413-426.

Tao, F., & Zhang, M. (2017). Digital twin shop-floor: a new shop-floor paradigm towards smart manufacturing. IEEE Access: Practical Innovations, Open Solutions, 5, 20418-20427.

Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., & Sui, F. (2017). Digital twin-driven product design, manufacturing and service with big data. International Journal of Advanced Manufacturing Technology,

Tao, F., Qi, Q., Wang, L., & Nee, A. Y. C. (2019a). Digital twins and cyber-physical systems toward smart manufacturing and industry 4.0: correlation and comparison. Engineering, 5(4), 653-661.

Tao, F., Sui, F., Liu, A., Qi, Q., Zhang, M., Song, B., Guo, Z., Lu, S. C. Y., & Nee, A. Y. C. (2019b). Digital twin-driven product design framework. International Journal of Production Research, 57(12), 3935-3953.

Terkaj, W., Gaboardi, P., Trevisan, C., Tolio, T., & Urgo, M. (2019). A digital factory platform for the design of roll shop plants. CIRP Journal of Manufacturing Science and Technology, 26, 88-93.

Uhlemann, T. H. J., Lehmann, C., & Steinhilper, R. (2017a). The digital twin: realizing the cyber-physical production system for industry 4.0. Procedia CIRP, 61, 335-340.

Uhlemann, T. H. J., Schock, C., Lehmann, C., Freiberger, S., & Steinhilper, R. (2017b). The digital twin: demonstrating the potential of real time data acquisition in production systems. Procedia Manufacturing, 9, 113-120.

Vachalek, J., Bartalsky, L., Rovny, O., Sismisova, D., Morhac, M., & Loksik, M. (2017). The digital twin of an industrial production line within the industry 4.0 concept. In Proceedings of the 2017 21st International Conference on Process Control (PC 2017) (pp. 258-262). New York: IEEE.

Valckenaers, P. (2020). Perspective on holonic manufacturing systems: PROSA becomes ARTI. Computers in Industry, 120, 103226.

van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538.

Wang, K. J., Lee, T. L., & Hsu, Y. (2020). Revolution on digital twin technology: a patent research approach. International Journal of Advanced Manufacturing Technology, 107(11-12), 4687-4704.

Wang, A. X., & Wang, X. (2019a). Virtual reality of 3D digital factory based on coastal environment. Journal of Coastal Research, 83, 507-512.

Wang, X. V., & Wang, L. (2019b). Digital twin-based WEEE recycling, recovery and remanufacturing in the background of Industry 4.0. International Journal of Production Research, 57(12), 3892-3902.

Wang, Y., & Wu, Z. (2020). Model construction of planning and scheduling system based on digital twin. International Journal of Advanced Manufacturing Technology, 109(7–8), 2189-2203.

Xu, Y., Bondaletova, N. F., Kovalev, V. I., & Komrakov, A. V. (2018). Digital twin concept in managing industrial capital construction projects life cycle. In 2018 Eleventh International Conference "Management of Large-Scale System Development"(MLSD) (pp. 1-3). New York: IEEE.

Zhang, H., Yan, Q., & Wen, Z. (2020). Information modeling for cyber-physical production system based on digital twin and AutomationML. International Journal of Advanced Manufacturing Technology, 107(3-4), 1927-1945.

Zhang, J., Ding, G., Zou, Y., Qin, S., & Fu, J. (2019). Review of job shop scheduling research and its new perspectives under Industry 4.0. Journal of Intelligent Manufacturing, 30(4), 1809-1830.

Zhao, G., Cao, X., Xiao, W., Zhu, Y., & Cheng, K. (2019). Digital twin for NC machining using complete process information expressed by STEP-NC standard. In CACRE2019: Proceedings of the 2019 4th International Conference on Automation, Control and Robotics Engineering (ACM International Conference Proceeding Series). New York: ACM.

Zhuang, C., Liu, J., & Xiong, H. (2018). Digital twin-based smart production management and control framework for the complex product assembly shopfloor. International Journal of Advanced Manufacturing Technology, 96(1-4), 1149-1163.

Submitted date:

Accepted date:

609d71b5a9539565e1225753 pmd Articles
Links & Downloads


Share this page
Page Sections