Review

Immersive virtual reality application for intelligent manufacturing: Applications and art design

  • Received: 09 October 2022 Revised: 27 November 2022 Accepted: 30 November 2022 Published: 23 December 2022
  • Intelligent manufacturing (IM), sometimes referred to as smart manufacturing (SM), is the use of real-time data analysis, machine learning, and artificial intelligence (AI) in the production process to achieve the aforementioned efficiencies. Human-machine interaction technology has recently been a hot issue in smart manufacturing. The unique interactivity of virtual reality (VR) innovations makes it possible to create a virtual world and allow users to communicate with that environment, providing users with an interface to be immersed in the digital world of the smart factory. And virtual reality technology aims to stimulate the imagination and creativity of creators to the maximum extent possible for reconstructing the natural world in a virtual environment, generating new emotions, and transcending time and space in the familiar and unfamiliar virtual world. Recent years have seen a great leap in the development of intelligent manufacturing and virtual reality technologies, yet little research has been done to combine the two popular trends. To fill this gap, this paper specifically employs Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines to conduct a systematic review of the applications of virtual reality in smart manufacturing. Moreover, the practical challenges and the possible future direction will also be covered.

    Citation: Yu Lei, Zhi Su, Xiaotong He, Chao Cheng. Immersive virtual reality application for intelligent manufacturing: Applications and art design[J]. Mathematical Biosciences and Engineering, 2023, 20(3): 4353-4387. doi: 10.3934/mbe.2023202

    Related Papers:

  • Intelligent manufacturing (IM), sometimes referred to as smart manufacturing (SM), is the use of real-time data analysis, machine learning, and artificial intelligence (AI) in the production process to achieve the aforementioned efficiencies. Human-machine interaction technology has recently been a hot issue in smart manufacturing. The unique interactivity of virtual reality (VR) innovations makes it possible to create a virtual world and allow users to communicate with that environment, providing users with an interface to be immersed in the digital world of the smart factory. And virtual reality technology aims to stimulate the imagination and creativity of creators to the maximum extent possible for reconstructing the natural world in a virtual environment, generating new emotions, and transcending time and space in the familiar and unfamiliar virtual world. Recent years have seen a great leap in the development of intelligent manufacturing and virtual reality technologies, yet little research has been done to combine the two popular trends. To fill this gap, this paper specifically employs Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines to conduct a systematic review of the applications of virtual reality in smart manufacturing. Moreover, the practical challenges and the possible future direction will also be covered.



    加载中


    [1] L. K. Johnson, Smart intelligence, Foreign Policy, (1992), 53–69.
    [2] J. Wang, C. Xu, J. Zhang, R. Zhong, Big data analytics for intelligent manufacturing systems: A review, J. Manuf Syst., (2021). https://doi.org/10.1016/j.jmsy.2021.03.005
    [3] W. H. Zijm, Towards intelligent manufacturing planning and control systems, OR-Spektrum, 22 (2000), 313–345. https://doi.org/10.1007/s002919900032 doi: 10.1007/s002919900032
    [4] W. Qi H. Su, A cybertwin based multimodal network for ecg patterns monitoring using deep learning, IEEE Trans. Industr. Inform., (2022). https://doi.org/10.1109/TII.2022.3159583
    [5] L. Monostori, J. Prohaszka, A step towards intelligent manufacturing: Modelling and monitoring of manufacturing processes through artificial neural networks, CIRP Ann., 42 (1993), 485–488. https://doi.org/10.1016/S0007-8506(07)62491-3 doi: 10.1016/S0007-8506(07)62491-3
    [6] X. Yao, J. Zhou, J. Zhang, C. R. Boër, From intelligent manufacturing to smart manufacturing for industry 4.0 driven by next generation artificial intelligence and further on, in 2017 5th international conference on enterprise systems (ES). IEEE, (2017), 311–318. https://doi.org/10.1109/ES.2017.58
    [7] J. Yi, C. Lu, G. Li, A literature review on latest developments of harmony search and its applications to intelligent manufacturing, Math. Biosci. Eng., 16 (2019), 2086–2117. https://doi.org/10.3934/mbe.2019102 doi: 10.3934/mbe.2019102
    [8] S. Shan, X. Wen, Y. Wei, Z. Wang, Y. Chen, Intelligent manufacturing in industry 4.0: A case study of sany heavy industry, Syst. Res. Behav. Sci., 37 (2020), 679–690. https://doi.org/10.1002/sres.2709 doi: 10.1002/sres.2709
    [9] H. Yoshikawa, Manufacturing and the 21st century intelligent manufacturing systems and the renaissance of the manufacturing industry, Technol. Forecast Soc. Change, 49 (1995), 195–213. https://doi.org/10.1016/0040-1625(95)00008-X doi: 10.1016/0040-1625(95)00008-X
    [10] J. Zheng, K. Chan, I. Gibson, Virtual reality, IEEE Potent., 17 (1998), 20–23.
    [11] M. J. Schuemie, P. Van Der Straaten, M. Krijn, C. A. Van Der Mast, Research on presence in virtual reality: A survey, Cyberpsychol. & Behav., 4 (2001), 183–201. https://doi.org/10.1089/109493101300117884 doi: 10.1089/109493101300117884
    [12] C. Anthes, R. J. García-Hernández, M. Wiedemann, D. Kranzlmüller, State of the art of virtual reality technology, in IEEE Aerosp. Conf.. (2016), 1–19. 10.1109/AERO.2016.7500674
    [13] F. Biocca, B. Delaney, Immersive virtual reality technology, Communication in the age of virtual reality, 15 (1995). https://doi.org/10.4324/9781410603128
    [14] T. Mazuryk, M. Gervautz, Virtual reality-history, applications, technology and future, 1996.
    [15] N.-N. Zhou, Y.-L. Deng, Virtual reality: A state-of-the-art survey, Int. J. Autom. Comput., 6 (2009), 319–325. https://doi.org/10.1007/s11633-009-0319-9 doi: 10.1007/s11633-009-0319-9
    [16] J. Egger, T. Masood, Augmented reality in support of intelligent manufacturing–a systematic literature review, Comput. Ind. Eng., 140 (2020), 106195. https://doi.org/10.1016/j.cie.2019.106195 doi: 10.1016/j.cie.2019.106195
    [17] B.-H. Li, B.-C. Hou, W.-T. Yu, X.-B. Lu, C.-W. Yang, Applications of artificial intelligence in intelligent manufacturing: a review, Front. Inform. Tech. El., 18 (2017), 86–96. https://doi.org/10.1631/FITEE.1601885 doi: 10.1631/FITEE.1601885
    [18] B. He, K.-J. Bai, Digital twin-based sustainable intelligent manufacturing: A review, Adv. Manuf., 9 (2021), 1–21. https://doi.org/10.1007/s40436-020-00302-5 doi: 10.1007/s40436-020-00302-5
    [19] G.-J. Cheng, L.-T. Liu, X.-J. Qiang, Y. Liu, Industry 4.0 development and application of intelligent manufacturing, in 2016 international conference on information system and artificial intelligence (ISAI). IEEE, (2016), 407–410. https://doi.org/10.1109/ISAI.2016.0092
    [20] G. Y. Tian, G. Yin, D. Taylor, Internet-based manufacturing: A review and a new infrastructure for distributed intelligent manufacturing, J. Intell. Manuf., 13 (2002), 323–338. https://doi.org/10.1023/A:1019907906158 doi: 10.1023/A:1019907906158
    [21] H. Su, W. Qi, J. Chen, D. Zhang, Fuzzy approximation-based task-space control of robot manipulators with remote center of motion constraint, IEEE Trans. Fuzzy Syst., 30 (2022), 1564–1573. https://doi.org/10.1109/TFUZZ.2022.3157075 doi: 10.1109/TFUZZ.2022.3157075
    [22] M.-S. Yoh, The reality of virtual reality, in Proceedings seventh international conference on virtual systems and multimedia. IEEE, (2001), 666–674. https://doi.org/10.1109/VSMM.2001.969726
    [23] V. Antoniou, F. L. Bonali, P. Nomikou, A. Tibaldi, P. Melissinos, F. P. Mariotto, et al., Integrating virtual reality and gis tools for geological mapping, data collection and analysis: An example from the metaxa mine, santorini (greece), Appl. Sci., 10 (2020), 8317. https://doi.org/10.3390/app10238317 doi: 10.3390/app10238317
    [24] A. Kunz, M. Zank, T. Nescher, K. Wegener, Virtual reality based time and motion study with support for real walking, Proced. CIRP, 57 (2016), 303–308. https://doi.org/10.1016/j.procir.2016.11.053 doi: 10.1016/j.procir.2016.11.053
    [25] M. Serras, L. G.-Sardia, B. Simes, H. lvarez, J. Arambarri, Dialogue enhanced extended reality: Interactive system for the operator 4.0, Appl. Sci., 10 (2020). https://doi.org/10.3390/app10113960
    [26] A. G. da Silva, M. V. M. Gomes, I. Winkler, Virtual reality and digital human modeling for ergonomic assessment in industrial product development: A patent and literature review, Appl. Sci., 12 (2022), 1084. https://doi.org/10.3390/app12031084 doi: 10.3390/app12031084
    [27] J. Kim, J. Jeong, Design and implementation of opc ua-based vr/ar collaboration model using cps server for vr engineering process, Appl. Sci., 12 (2022), 7534. https://doi.org/10.3390/app12157534 doi: 10.3390/app12157534
    [28] J.-d.-J. Cordero-Guridi, L. Cuautle-Gutiérrez, R.-I. Alvarez-Tamayo, S.-O. Caballero-Morales, Design and development of a i4. 0 engineering education laboratory with virtual and digital technologies based on iso/iec tr 23842-1 standard guidelines, Appl. Sci., 12 (2022), 5993. https://doi.org/10.3390/app12125993 doi: 10.3390/app12125993
    [29] H. Heinonen, A. Burova, S. Siltanen, J. Lähteenmäki, J. Hakulinen, M. Turunen, Evaluating the benefits of collaborative vr review for maintenance documentation and risk assessment, Appl. Sci., 12 (2022), 7155. https://doi.org/10.3390/app12147155 doi: 10.3390/app12147155
    [30] V. Settgast, K. Kostarakos, E. Eggeling, M. Hartbauer, T. Ullrich, Product tests in virtual reality: Lessons learned during collision avoidance development for drones, Designs, 6 (2022), 33. https://doi.org/10.3390/designs6020033 doi: 10.3390/designs6020033
    [31] D. Mourtzis, J. Angelopoulos, N. Panopoulos, Smart manufacturing and tactile internet based on 5g in industry 4.0: Challenges, applications and new trends, Electronics-Switz, 10 (2021), 3175. https://doi.org/10.3390/electronics10243175 doi: 10.3390/electronics10243175
    [32] Y. Saito, K. Kawashima, M. Hirakawa, Effectiveness of a head movement interface for steering a vehicle in a virtual reality driving simulation, Symmetry, 12 (2020), 1645. https://doi.org/10.3390/sym12101645 doi: 10.3390/sym12101645
    [33] Y.-P. Su, X.-Q. Chen, T. Zhou, C. Pretty, G. Chase, Mixed-reality-enhanced human–robot interaction with an imitation-based mapping approach for intuitive teleoperation of a robotic arm-hand system, Appl. Sci., 12 (2022), 4740. https://doi.org/10.3390/app12094740 doi: 10.3390/app12094740
    [34] F. Arena, M. Collotta, G. Pau, F. Termine, An overview of augmented reality, Computers, 11 (2022), 28. https://doi.org/10.3390/computers11020028 doi: 10.3390/computers11020028
    [35] P. C. Thomas, W. David, Augmented reality: An application of heads-up display technology to manual manufacturing processes, in Hawaii international conference on system sciences, 2. ACM SIGCHI Bulletin New York, NY, USA, 1992.
    [36] J. Safari Bazargani, A. Sadeghi-Niaraki, S.-M. Choi, Design, implementation, and evaluation of an immersive virtual reality-based educational game for learning topology relations at schools: A case study, Sustainability-Basel, 13 (2021), 13066. https://doi.org/10.3390/su132313066 doi: 10.3390/su132313066
    [37] K. Židek, J. Pitel', M. Balog, A. Hošovskỳ, V. Hladkỳ, P. Lazorík, et al., CNN training using 3d virtual models for assisted assembly with mixed reality and collaborative robots, Appl. Sci., 11 (2021), 4269. https://doi.org/10.3390/app11094269 doi: 10.3390/app11094269
    [38] S. Mandal, Brief introduction of virtual reality & its challenges, Int. J. Sci. Eng. Res., 4 (2013), 304–309.
    [39] D. Rose, N. Foreman, Virtual reality. The Psycho., (1999).
    [40] G. Riva, C. Malighetti, A. Chirico, D. Di Lernia, F. Mantovani, A. Dakanalis, Virtual reality, in Rehabilitation interventions in the patient with obesity. Springer, (2020), 189–204.
    [41] J. N. Latta, D. J. Oberg, A conceptual virtual reality model, IEEE Comput. Graph. Appl., 14 (1994), 23–29. https://doi.org/10.1109/38.250915 doi: 10.1109/38.250915
    [42] J. Lanier, Virtual reality: The promise of the future. Interactive Learning International, 8 (1992), 275–79.
    [43] S. Serafin, C. Erkut, J. Kojs, N. C. Nilsson, R. Nordahl, Virtual reality musical instruments: State of the art, design principles, and future directions, Comput. Music. J., 40 (2016). https://doi.org/10.1162/COMJ_a_00372
    [44] W. Qi, H. Su, A. Aliverti, A smartphone-based adaptive recognition and real-time monitoring system for human activities, IEEE Trans. Hum. Mach. Syst., 50 (2020), 414 - 423. https://doi.org/10.1109/THMS.2020.2984181 doi: 10.1109/THMS.2020.2984181
    [45] P. Kopacek, Intelligent manufacturing: present state and future trends, J. Intell. Robot. Syst., 26 (1999), 217–229. https://doi.org/10.1023/A:1008168605803 doi: 10.1023/A:1008168605803
    [46] Y. Feng, Y. Zhao, H. Zheng, Z. Li, J. Tan, Data-driven product design toward intelligent manufacturing: A review, Int. J. Adv. Robot. Syst., 17 (2020), 1729881420911257. https://doi.org/10.1177/1729881420911257 doi: 10.1177/1729881420911257
    [47] H. Su, W. Qi, Y. Hu, H. R. Karimi, G. Ferrigno, E. De Momi, An incremental learning framework for human-like redundancy optimization of anthropomorphic manipulators, IEEE Trans. Industr. Inform., 18 (2020), 1864–1872. https://doi.org/10.1109/TII.2020.3036693 doi: 10.1109/TII.2020.3036693
    [48] E. Hozdić, Smart factory for industry 4.0: A review, Int. J. Adv. Manuf. Technol., 7 (2015), 28–35.
    [49] R. Burke, A. Mussomeli, S. Laaper, M. Hartigan, B. Sniderman, The smart factory: Responsive, adaptive, connected manufacturing, Deloitte Insights, 31 (2017), 1–10.
    [50] R. Y. Zhong, X. Xu, E. Klotz, S. T. Newman, Intelligent manufacturing in the context of industry 4.0: a review, Engineering-Prc, 3 (2017), 616–630. https://doi.org/10.1016/J.ENG.2017.05.015 doi: 10.1016/J.ENG.2017.05.015
    [51] A. Kusiak, Intelligent manufacturing, System, Prentice-Hall, Englewood Cliffs, NJ, (1990).
    [52] G. Rzevski, A framework for designing intelligent manufacturing systems, Comput. Ind., 34 (1997), 211–219. https://doi.org/10.1016/S0166-3615(97)00056-0 doi: 10.1016/S0166-3615(97)00056-0
    [53] E. Oztemel, Intelligent manufacturing systems, in Artificial intelligence techniques for networked manufacturing enterprises management. Springer, (2010), pp. 1–41. https://doi.org/10.1007/978-1-84996-119-6_1
    [54] J. Zhou, P. Li, Y. Zhou, B. Wang, J. Zang, L. Meng, Toward new-generation intelligent manufacturing, Engineering-Prc, 4 (2018), 11–20. https://doi.org/10.1016/j.eng.2018.01.002 doi: 10.1016/j.eng.2018.01.002
    [55] R. Y. Zhong, X. Xu, E. Klotz, S. T. Newman, Intelligent manufacturing in the context of industry 4.0: a review, Engineering-Prc, 3 (2017), 616–630. https://doi.org/10.1016/J.ENG.2017.05.015 doi: 10.1016/J.ENG.2017.05.015
    [56] H. S. Kang, J. Y. Lee, S. Choi, H. Kim, J. H. Park, J. Y. Son, B. H. Kim, S. D. Noh, Smart manufacturing: Past research, present findings, and future directions, Int. J. Pr. Eng. Man-Gt., 3 (2016), 111–128. https://doi.org/10.1007/s40684-016-0015-5 doi: 10.1007/s40684-016-0015-5
    [57] R. Jardim-Goncalves, D. Romero, A. Grilo, Factories of the future: challenges and leading innovations in intelligent manufacturing, Int. J. Comput. Integr. Manuf., 30 (2017), 4–14.
    [58] A. Kusiak, Smart manufacturing, Int. J. Prod. Res., 56 (2018), 508–517. https://doi.org/10.1080/00207543.2017.1351644
    [59] B. Wang, F. Tao, X. Fang, C. Liu, Y. Liu, T. Freiheit, Smart manufacturing and intelligent manufacturing: A comparative review, Engineering-Prc, 7 (2021), 738–757. https://doi.org/10.1016/j.eng.2020.07.017 doi: 10.1016/j.eng.2020.07.017
    [60] P. Zheng, Z. Sang, R. Y. Zhong, Y. Liu, C. Liu, K. Mubarok, et al., Smart manufacturing systems for industry 4.0: Conceptual framework, scenarios, and future perspectives, Front. Mech. Eng., 13 (2018), 137–150. https://doi.org/10.1007/s11465-018-0499-5 doi: 10.1007/s11465-018-0499-5
    [61] P. Osterrieder, L. Budde, T. Friedli, The smart factory as a key construct of industry 4.0: A systematic literature review, Int. J. Prod. Econ., 221 107476. https://doi.org/10.1016/j.ijpe.2019.08.011
    [62] D. Guo, M. Li, R. Zhong, G. Q. Huang, Graduation intelligent manufacturing system (gims): an industry 4.0 paradigm for production and operations management, Ind. Manage. Data Syst., (2020). https://doi.org/10.1108/IMDS-08-2020-0489
    [63] A. Barari, M. de Sales Guerra Tsuzuki, Y. Cohen, M. Macchi, Intelligent manufacturing systems towards industry 4.0 era, J. Intell. Manuf., 32 (2021), 1793–1796. https://doi.org/10.1007/s10845-021-01769-0 doi: 10.1007/s10845-021-01769-0
    [64] C. Christo, C. Cardeira, Trends in intelligent manufacturing systems, in 2007 IEEE International Symposium on Industrial Electronics-Switz.. IEEE, (2007), 3209–3214. https://doi.org/10.1109/ISIE.2007.4375129
    [65] M.-P. Pacaux-Lemoine, D. Trentesaux, G. Z. Rey, P. Millot, Designing intelligent manufacturing systems through human-machine cooperation principles: A human-centered approach, Comput. Ind. Eng., 111 (2017), 581–595. https://doi.org/10.1016/j.cie.2017.05.014 doi: 10.1016/j.cie.2017.05.014
    [66] W. F. Gaughran, S. Burke, P. Phelan, Intelligent manufacturing and environmental sustainability, Robot. Comput. Integr. Manuf., 23 (2007), 704–711. https://doi.org/10.1016/j.rcim.2007.02.016 doi: 10.1016/j.rcim.2007.02.016
    [67] Y. Boas, Overview of virtual reality technologies, in Inter. Mult. Confer., 2013 (2013).
    [68] lvaro Segura, H. V. Diez, I. Barandiaran, A. Arbelaiz, H. lvarez, B. Simes, J. Posada, A. Garca-Alonso, R. Ugarte, Visual computing technologies to support the operator 4.0, Comput. Ind. Eng., 139 (2020), 105550. https://doi.org/10.1016/j.cie.2018.11.060 doi: 10.1016/j.cie.2018.11.060
    [69] D. Romero, J. Stahre, T. Wuest, O. Noran, P. Bernus, Fasth, Fast-Berglund, D. Gorecky, Towards an operator 4.0 typology: A human-centric perspective on the fourth industrial revolution technologies, 10 (2016).
    [70] H. Qiao, J. Chen, X. Huang, A survey of brain-inspired intelligent robots: Integration of vision, decision, motion control, and musculoskeletal systems, " IEEE T. Cybernetics, 52 (2022), 11267 - 11280. https://doi.org/10.1109/TCYB.2021.3071312
    [71] F. Firyaguna, J. John, M. O. Khyam, D. Pesch, E. Armstrong, H. Claussen, H. V. Poor et al., Towards industry 5.0: Intelligent reflecting surface (irs) in smart manufacturing, arXiv preprint arXiv: 2201.02214, (2022). https://doi.org/10.1109/MCOM.001.2200016
    [72] A. M. Almassri, W. Wan Hasan, S. A. Ahmad, A. J. Ishak, A. Ghazali, D. Talib, C. Wada, Pressure sensor: state of the art, design, and application for robotic hand, J. Sensors, 2015 (2015). https://doi.org/10.1155/2015/846487
    [73] B. Munari, Design as art. Penguin UK, (2008).
    [74] B. De La Harpe, J. F. Peterson, N. Frankham, R. Zehner, D. Neale, E. Musgrave, R. McDermott, Assessment focus in studio: What is most prominent in architecture, art and design? IJADE., 28 (2009), 37–51. https://doi.org/10.1111/j.1476-8070.2009.01591.x
    [75] C. Gray, J. Malins, Visualizing research: A guide to the research process in art and design. Routledge, (2016).
    [76] M. Barnard, Art, design and visual culture: An introduction. Bloomsbury Publishing, (1998).
    [77] C. Crouch, Modernism in art, design and architecture. Bloomsbury Publishing, (1998).
    [78] M. Biggs, The role of the artefact in art and design research, Int. J. Des. Sci. Technol., 2002.
    [79] H. Su, W. Qi, Y. Schmirander, S. E. Ovur, S. Cai, X. Xiong, A human activity-aware shared control solution for medical human–robot interaction, Assembly Autom., (2022) ahead-of-print. https://doi.org/10.1108/AA-12-2021-0174
    [80] R. D. Gandhi, D. S. Patel, Virtual reality–opportunities and challenges, Virtual Real., 5 (2018).
    [81] A. J. Trappey, C. V. Trappey, M.-H. Chao, C.-T. Wu, Vr-enabled engineering consultation chatbot for integrated and intelligent manufacturing services, J. Ind. Inf. Integrat., 26 (2022), 100331. https://doi.org/10.1016/j.jii.2022.100331 doi: 10.1016/j.jii.2022.100331
    [82] K. Valaskova, M. Nagy, S. Zabojnik, G. Lăzăroiu, Industry 4.0 wireless networks and cyber-physical smart manufacturing systems as accelerators of value-added growth in slovak exports, Mathematics-Basel, 10 (2022), 2452. https://doi.org/10.3390/math10142452 doi: 10.3390/math10142452
    [83] J. de Assis Dornelles, N. F. Ayala, A. G. Frank, Smart working in industry 4.0: How digital technologies enhance manufacturing workers' activities, Comput. Ind. Eng., 163 (2022), 107804. https://doi.org/10.1016/j.cie.2021.107804 doi: 10.1016/j.cie.2021.107804
    [84] V. Tripathi, S. Chattopadhyaya, A. K. Mukhopadhyay, S. Sharma, C. Li, S. Singh, W. U. Hussan, B. Salah, W. Saleem, A. Mohamed, A sustainable productive method for enhancing operational excellence in shop floor management for industry 4.0 using hybrid integration of lean and smart manufacturing: An ingenious case study, Sustainability-Basel, 14 (2022), 7452. https://doi.org/10.3390/su14127452 doi: 10.3390/su14127452
    [85] S. M. M. Sajadieh, Y. H. Son, S. D. Noh, A conceptual definition and future directions of urban smart factory for sustainable manufacturing, Sustainability-Basel, 14 (2022), 1221. https://doi.org/10.3390/su14031221 doi: 10.3390/su14031221
    [86] Y. H. Son, G.-Y. Kim, H. C. Kim, C. Jun, S. D. Noh, Past, present, and future research of digital twin for smart manufacturing, J. Comput. Des. Eng., 9 (2022), 1–23. https://doi.org/10.1093/jcde/qwab067 doi: 10.1093/jcde/qwab067
    [87] G. Moiceanu, G. Paraschiv, Digital twin and smart manufacturing in industries: A bibliometric analysis with a focus on industry 4.0, Sensors-Basel, 22 (2022), 1388. https://doi.org/10.3390/s22041388 doi: 10.3390/s22041388
    [88] K. Cheng, Q. Wang, D. Yang, Q. Dai, M. Wang, Digital-twins-driven semi-physical simulation for testing and evaluation of industrial software in a smart manufacturing system, Machines, 10 (2022), 388. https://doi.org/10.3390/machines10050388 doi: 10.3390/machines10050388
    [89] S. Arjun, L. Murthy, P. Biswas, Interactive sensor dashboard for smart manufacturing, Procedia Comput. Sci., 200 (2022), 49–61. https://doi.org/10.1016/j.procs.2022.01.204 doi: 10.1016/j.procs.2022.01.204
    [90] J. Yang, Y. H. Son, D. Lee, S. D. Noh, Digital twin-based integrated assessment of flexible and reconfigurable automotive part production lines, Machines, 10 (2022), 75. https://doi.org/10.3390/machines10020075 doi: 10.3390/machines10020075
    [91] J. Friederich, D. P. Francis, S. Lazarova-Molnar, N. Mohamed, A framework for data-driven digital twins for smart manufacturing, Comput. Ind., 136 (2022), 103586. https://doi.org/10.1016/j.compind.2021.103586 doi: 10.1016/j.compind.2021.103586
    [92] L. Li, B. Lei, C. Mao, Digital twin in smart manufacturing, J. Ind. Inf. Integr., 26 (2022), 100289. https://doi.org/10.1016/j.jii.2021.100289 doi: 10.1016/j.jii.2021.100289
    [93] D. Nåfors, B. Johansson, Virtual engineering using realistic virtual models in brownfield factory layout planning, Sustainability-Basel, 13 (2021), 11102. https://doi.org/10.3390/su131911102 doi: 10.3390/su131911102
    [94] A. Geiger, E. Brandenburg, R. Stark, Natural virtual reality user interface to define assembly sequences for digital human models, Appl. System Innov., 3 (2020), 15. https://doi.org/10.3390/asi3010015 doi: 10.3390/asi3010015
    [95] G. Gabajova, B. Furmannova, I. Medvecka, P. Grznar, M. Krajčovič, R. Furmann, Virtual training application by use of augmented and virtual reality under university technology enhanced learning in slovakia, Sustainability-Basel, 11 (2019), 6677. https://doi.org/10.3390/su11236677 doi: 10.3390/su11236677
    [96] W. Qi, S. E. Ovur, Z. Li, A. Marzullo, R. Song, Multi-sensor guided hand gesture recognition for a teleoperated robot using a recurrent neural network, IEEE Robot Autom Lett., 6 (2021), 6039–6045. https://doi.org/10.1109/LRA.2021.3089999 doi: 10.1109/LRA.2021.3089999
    [97] L. Pérez, S. Rodríguez-Jiménez, N. Rodríguez, R. Usamentiaga, D. F. García, Digital twin and virtual reality based methodology for multi-robot manufacturing cell commissioning, Appl. Sci., 10 (2020), 3633. https://doi.org/10.3390/app10103633 doi: 10.3390/app10103633
    [98] J. Mora-Serrano, F. Muñoz-La Rivera, I. Valero, Factors for the automation of the creation of virtual reality experiences to raise awareness of occupational hazards on construction sites, Electronics-Switz., 10 (2021), 1355. https://doi.org/10.3390/electronics10111355 doi: 10.3390/electronics10111355
    [99] C. McDonald, K. A. Campbell, C. Benson, M. J. Davis, C. J. Frost, Workforce development and multiagency collaborations: A presentation of two case studies in child welfare, Sustainability-Basel, 13 (2021), 10190. https://doi.org/10.3390/su131810190 doi: 10.3390/su131810190
    [100] Z. Xu, N. Zheng, Incorporating virtual reality technology in safety training solution for construction site of urban cities, Sustainability-Basel, 13 (2020), 243. https://doi.org/10.3390/su13010243 doi: 10.3390/su13010243
    [101] L. Frizziero, L. Galletti, L. Magnani, E. G. Meazza, M. Freddi, Blitz vision: Development of a new full-electric sports sedan using qfd, sde and virtual prototyping, Inventions, 7 (2022), 41. https://doi.org/10.3390/inventions7020041 doi: 10.3390/inventions7020041
    [102] N. Lyons, Deep learning-based computer vision algorithms, immersive analytics and simulation software, and virtual reality modeling tools in digital twin-driven smart manufacturing, Econom. Manag. Financ. Markets, 17 (2022).
    [103] H. Qiao, S. Zhong, Z. Chen, H. Wang, Improving performance of robots using human-inspired approaches: A survey, Sci. China Inf. Sci., 65 (2022), 221201. https://doi.org/10.1007/s11432-022-3606-1 doi: 10.1007/s11432-022-3606-1
    [104] H. Su, A. Mariani, S. E. Ovur, A. Menciassi, G. Ferrigno, E. De Momi, Toward teaching by demonstration for robot-assisted minimally invasive surgery, IEEE Trans. Autom, 18 (2021), 484 - 494. https://doi.org/10.1109/TASE.2020.3045655 doi: 10.1109/TASE.2020.3045655
    [105] H. Su, W. Qi, Z. Li, Z. Chen, G. Ferrigno, E. De Momi, Deep neural network approach in EMG-based force estimation for human–robot interaction, IEEE Trans. Artif. Intell., 2 (2021), 404 - 412. https://doi.org/10.1109/TAI.2021.3066565 doi: 10.1109/TAI.2021.3066565
    [106] A. A. Malik, T. Masood, A. Bilberg, Virtual reality in manufacturing: immersive and collaborative artificial-reality in design of human-robot workspace, Int. J. Comput. Integr. Manuf., 33 (2020), 22–37. https://doi.org/10.1080/0951192X.2019.1690685 doi: 10.1080/0951192X.2019.1690685
    [107] A. Corallo, A. M. Crespino, M. Lazoi, M. Lezzi, Model-based big data analytics-as-a-service framework in smart manufacturing: A case study, Robot. Comput. Integr. Manuf., 76 (2022), 102331. https://doi.org/10.1016/j.rcim.2022.102331 doi: 10.1016/j.rcim.2022.102331
    [108] Y.-M. Tang, G. T. S. Ho, Y.-Y. Lau, S.-Y. Tsui, Integrated smart warehouse and manufacturing management with demand forecasting in small-scale cyclical industries, Machines, 10 (2022), 472. https://doi.org/10.3390/machines10060472 doi: 10.3390/machines10060472
    [109] M. Samardžić, D. Stefanović, U. Marjanović, Transformation towards smart working: Research proposal, in 2022 21st International Symposium INFOTEH-JAHORINA (INFOTEH). IEEE, (2022), 1–6. https://doi.org/10.1109/INFOTEH53737.2022.9751256
    [110] T. Caporaso, S. Grazioso, G. Di Gironimo, Development of an integrated virtual reality system with wearable sensors for ergonomic evaluation of human–robot cooperative workplaces, Sensors-Basel, 22 (2022), 2413. https://doi.org/10.3390/s22062413 doi: 10.3390/s22062413
    [111] W. Qi, N. Wang, H. Su, A. Aliverti DCNN based human activity recognition framework with depth vision guiding, Neurocomputing, 486 (2022), 261–271. https://doi.org/10.1016/j.neucom.2021.11.044 doi: 10.1016/j.neucom.2021.11.044
    [112] W. Zhu, X. Fan, Y. Zhang, Applications and research trends of digital human models in the manufacturing industry, VRIH, 1 (2019), 558–579. https://doi.org/10.1016/j.vrih.2019.09.005 doi: 10.1016/j.vrih.2019.09.005
    [113] O. Robert, P. Iztok, B. Borut, Real-time manufacturing optimization with a simulation model and virtual reality, Procedia Manuf., 38 (2019), 1103–1110. https://doi.org/10.1016/j.promfg.2020.01.198 doi: 10.1016/j.promfg.2020.01.198
    [114] I. Kačerová, J. Kubr, P. Hořejší, J. Kleinová, Ergonomic design of a workplace using virtual reality and a motion capture suit, Appl. Sci., 12 (2022), 2150. https://doi.org/10.3390/app12042150 doi: 10.3390/app12042150
    [115] M. Woschank, D. Steinwiedder, A. Kaiblinger, P. Miklautsch, C. Pacher, H. Zsifkovits, The integration of smart systems in the context of industrial logistics in manufacturing enterprises, Procedia Comput. Sci., 200 (2022), 727–737. https://doi.org/10.1016/j.procs.2022.01.271 doi: 10.1016/j.procs.2022.01.271
    [116] A. Umbrico, A. Orlandini, A. Cesta, M. Faroni, M. Beschi, N. Pedrocchi, A. Scala, P. Tavormina, S. Koukas, A. Zalonis et al., Design of advanced human–robot collaborative cells for personalized human–robot collaborations, Appl. Sci., 12 (2022), 6839. https://doi.org/10.3390/app12146839 doi: 10.3390/app12146839
    [117] W. Qi, A. Aliverti, A multimodal wearable system for continuous and real-time breathing pattern monitoring during daily activity, IEEE JBHI., 24 (2019), 2199–2207. https://doi.org/10.1109/JBHI.2019.2963048 doi: 10.1109/JBHI.2019.2963048
    [118] J. M. Runji, Y.-J. Lee, C.-H. Chu, User requirements analysis on augmented reality-based maintenance in manufacturing, J. Comput. Inf. Sci. Eng., 22 (2022), 050901. https://doi.org/10.1115/1.4053410 doi: 10.1115/1.4053410
    [119] D. Wuttke, A. Upadhyay, E. Siemsen, A. Wuttke-Linnemann, Seeing the bigger picture? ramping up production with the use of augmented reality, Manuf. Serv. Oper. Manag., (2022). https://doi.org/10.1287/msom.2021.1070
    [120] M. Catalano, A. Chiurco, C. Fusto, L. Gazzaneo, F. Longo, G. Mirabelli, L. Nicoletti, V. Solina, S. Talarico, A digital twin-driven and conceptual framework for enabling extended reality applications: A case study of a brake discs manufacturer, Procedia Comput. Sci., 200 (2022), 1885–1893. https://doi.org/10.1016/j.procs.2022.01.389 doi: 10.1016/j.procs.2022.01.389
    [121] J. S. Devagiri, S. Paheding, Q. Niyaz, X. Yang, S. Smith, Augmented reality and artificial intelligence in industry: Trends, tools, and future challenges, Expert Syst. Appl., (2022), 118002. https://doi.org/10.1016/j.eswa.2022.118002
    [122] P. T. Ho, J. A. Albajez, J. Santolaria, J. A. Yagüe-Fabra, Study of augmented reality based manufacturing for further integration of quality control 4.0: A systematic literature review, Appl. Sci., 12 (2022), 1961. https://doi.org/10.3390/app12041961 doi: 10.3390/app12041961
    [123] Z.-H. Lai, W. Tao, M. C. Leu, Z. Yin, Smart augmented reality instructional system for mechanical assembly towards worker-centered intelligent manufacturing, J. Manuf. Syst., 55 (2020), 69–81. https://doi.org/10.1016/j.jmsy.2020.02.010 doi: 10.1016/j.jmsy.2020.02.010
    [124] J. Xiong, E.-L. Hsiang, Z. He, T. Zhan, S.-T. Wu, Augmented reality and virtual reality displays: emerging technologies and future perspectives, Light Sci. Appl., 10 (2021), 1–30. https://doi.org/10.1038/s41377-021-00658-8 doi: 10.1038/s41377-021-00658-8
    [125] M.-G. Kim, J. Kim, S. Y. Chung, M. Jin, M. J. Hwang, Robot-based automation for upper and sole manufacturing in shoe production, Machines, 10 (2022), 255. https://doi.org/10.3390/machines10040255 doi: 10.3390/machines10040255
    [126] P. Grefen, I. Vanderfeesten, K. Traganos, Z. Domagala-Schmidt, J. van der Vleuten, Advancing smart manufacturing in europe: Experiences from two decades of research and innovation projects, Machines, 10 (2022), 45. https://doi.org/10.3390/machines10010045 doi: 10.3390/machines10010045
    [127] Y. Zhou, J. Zang, Z. Miao, T. Minshall, Upgrading pathways of intelligent manufacturing in china: Transitioning across technological paradigms, Engineering-Prc, 5 (2019), 691–701. https://doi.org/10.1016/j.eng.2019.07.016 doi: 10.1016/j.eng.2019.07.016
    [128] K. S. Kiangala, Z. Wang, An experimental safety response mechanism for an autonomous moving robot in a smart manufacturing environment using q-learning algorithm and speech recognition, Sensors-Basel, 22 (2022), 941. https://doi.org/10.3390/s22030941 doi: 10.3390/s22030941
    [129] S. Fernandes, Which way to cope with covid-19 challenges? contributions of the iot for smart city projects, Big Data Cogn. Comput., 5 (2021), 26. https://doi.org/10.3390/bdcc5020026 doi: 10.3390/bdcc5020026
    [130] C. Thomay, U. Bodin, H. Isakovic, R. Lasch, N. Race, C. Schmittner, G. Schneider, Z. Szepessy, M. Tauber, Z. Wang, Towards adaptive quality assurance in industrial applications, in 2022 IEEE/IFIP NOMS.. IEEE, (2022), 1–6. https://doi.org/10.1109/NOMS54207.2022.9789928
    [131] D. Stadnicka, P. Litwin, D. Antonelli, Human factor in intelligent manufacturing systems-knowledge acquisition and motivation, Proced. CIRP, 79 (2019), 718–723. https://doi.org/10.1016/j.procir.2019.02.023 doi: 10.1016/j.procir.2019.02.023
    [132] H.-X. Li, H. Si, Control for intelligent manufacturing: A multiscale challenge, Engineering-Prc, 3 (2017), 608–615. https://doi.org/10.1016/J.ENG.2017.05.016 doi: 10.1016/J.ENG.2017.05.016
    [133] T. Kalsoom, N. Ramzan, S. Ahmed, M. Ur-Rehman, Advances in sensor technologies in the era of smart factory and industry 4.0, Sensors-Basel, 20 (2020), 6783. https://doi.org/10.3390/s20236783 doi: 10.3390/s20236783
    [134] J. Radianti, T. A. Majchrzak, J. Fromm, I. Wohlgenannt, A systematic review of immersive virtual reality applications for higher education: Design elements, lessons learned, and research agenda, Comput. Educ., 147 (2020), COMPUT EDUC103778. https://doi.org/10.1016/j.compedu.2019.103778 doi: 10.1016/j.compedu.2019.103778
    [135] D. Kamińska, T. Sapiński, S. Wiak, T. Tikk, R. E. Haamer, E. Avots, A. Helmi, C. Ozcinar, G. Anbarjafari, Virtual reality and its applications in education: Survey, Information, 10 (2019), 318. https://doi.org/10.3390/info10100318 doi: 10.3390/info10100318
    [136] T. Joda, G. Gallucci, D. Wismeijer, N. U. Zitzmann, Augmented and virtual reality in dental medicine: A systematic review, Comput. Biol. Med., 108 (2019), 93–100. https://doi.org/10.1016/j.compbiomed.2019.03.012 doi: 10.1016/j.compbiomed.2019.03.012
    [137] C. Li, Y. Chen, Y. Shang, A review of industrial big data for decision making in intelligent manufacturing, J. Eng. Sci. Technol., (2021). https://doi.org/10.1016/j.jestch.2021.06.001
    [138] L. Zhou, Z. Jiang, N. Geng, Y. Niu, F. Cui, K. Liu, N. Qi, Production and operations management for intelligent manufacturing: a systematic literature review, Int. J. Prod. Res., 60 (2022), 808–846. https://doi.org/10.1080/00207543.2021.2017055 doi: 10.1080/00207543.2021.2017055
    [139] L. Adriana Crdenas-Robledo, scar Hernndez-Uribe, C. Reta, J. Antonio Cantoral-Ceballos, Extended reality applications in industry 4.0. a systematic literature review, Telemat. Inform., 73 (2022), 101863. https://doi.org/10.1016/j.tele.2022.101863 doi: 10.1016/j.tele.2022.101863
    [140] Z. Wang, X. Bai, S. Zhang, M. Billinghurst, W. He, P. Wang, W. Lan, H. Min, Y. Chen, A comprehensive review of augmented reality-based instruction in manual assembly, training and repair, Robot. Comput. Integr. Manuf., 78 (2022), 102407. https://doi.org/10.1016/j.rcim.2022.102407 doi: 10.1016/j.rcim.2022.102407
    [141] N. Kumar, S. C. Lee, Human-machine interface in smart factory: A systematic literature review, Technol. Forecast. Soc. Change, 174 (2022), 121284. https://doi.org/10.1016/j.techfore.2021.121284 doi: 10.1016/j.techfore.2021.121284
    [142] M. Javaid, A. Haleem, R. P. Singh, R. Suman, Enabling flexible manufacturing system (fms) through the applications of industry 4.0 technologies, Int. Things Cyber-Phys. Syst., (2022). https://doi.org/10.1016/j.iotcps.2022.05.005
    [143] A. Künz, S. Rosmann, E. Loria, J. Pirker, The potential of augmented reality for digital twins: A literature review, in 2022 IEEE Conference on Virtual Reality and 3D User Interfaces (VR). IEEE, (2022), 389–398. https://doi.org/10.1109/VR51125.2022.00058
    [144] I. Shah, C. Doshi, M. Patel, S. Tanwar, W.-C. Hong, R. Sharma, A comprehensive review of the technological solutions to analyse the effects of pandemic outbreak on human lives, Medicina (Kaunas), 58 (2022), 311. https://doi.org/10.3390/medicina58020311 doi: 10.3390/medicina58020311
    [145] R. P. Singh, M. Javaid, R. Kataria, M. Tyagi, A. Haleem, R. Suman, Significant applications of virtual reality for covid-19 pandemic, Diabetes Metab. Syndr., 14 (2020), 661–664. https://doi.org/10.1016/j.dsx.2020.05.011 doi: 10.1016/j.dsx.2020.05.011
    [146] A. O. Kwok, S. G. Koh, Covid-19 and extended reality (xr), Curr. Issues Tour., 24 (2021), 1935–1940. https://doi.org/10.1080/13683500.2020.1798896 doi: 10.1080/13683500.2020.1798896
    [147] G. Czifra, Z. Molnár et al., Covid-19, industry 4.0, Research papers faculty of materials science and technology slovak university of technology, 28 (2020), 36–45. https://doi.org/10.2478/rput-2020-0005
    [148] Q. Yu-ming, D. San-peng et al., Research on intelligent manufacturing flexible production line system based on digital twin, in 2020 35th Youth Academic Annual Conference of Chinese Association of Automation (YAC), IEEE, (2020), 854–862. https://doi.org/10.1109/YAC51587.2020.9337500
    [149] L. O. Alpala, D. J. Quiroga-Parra, J. C. Torres, D. H. Peluffo-Ordóñez, Smart factory using virtual reality and online multi-user: Towards a metaverse for experimental frameworks, Appl. Sci., 12 (2022), 6258. https://doi.org/10.3390/app12126258 doi: 10.3390/app12126258
    [150] E. Chang, H. T. Kim, B. Yoo, Virtual reality sickness: A review of causes and measurements, Int. J. Hum-Comput. Int., 36 (2020), 1658–1682. https://doi.org/10.1080/10447318.2020.1778351 doi: 10.1080/10447318.2020.1778351
    [151] H. Su, W. Qi, C. Yang, J. Sandoval, G. Ferrigno, E. De Momi, Deep neural network approach in robot tool dynamics identification for bilateral teleoperation, IEEE Robot. Autom. Lett., 5 (2020), 2943–2949. https://doi.org/10.1109/LRA.2020.2974445 doi: 10.1109/LRA.2020.2974445
    [152] H. Su, Y. Hu, H. R. Karimi, A. Knoll, G. Ferrigno, E. De Momi, Improved recurrent neural network-based manipulator control with remote center of motion constraints: Experimental results, Neural Netw., 131 (2020), 291–299. https://doi.org/10.1016/j.neunet.2020.07.033 doi: 10.1016/j.neunet.2020.07.033
    [153] S. Phuyal, D. Bista, R. Bista, Challenges, opportunities and future directions of smart manufacturing: A state of art review, Sustain. Fut., 2 (2020), 100023. https://doi.org/10.1016/j.sftr.2020.100023 doi: 10.1016/j.sftr.2020.100023
  • Reader Comments
  • © 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(4024) PDF downloads(381) Cited by(4)

Article outline

Figures and Tables

Figures(9)  /  Tables(2)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog