Thematic Evolution of Virtual Manufacturing Literature (1983–2023): History
View Latest Version
Please note this is an old version of this entry, which may differ significantly from the current revision.
Contributor:
Ikpe Justice Akpan
,
Yawo Mamoua Kobara

Virtual manufacturing (VM) technology emerged in the 1980s as a revolutionary strategy to optimize and streamline the entire product/service manufacturing lifecycle. However, over the years, its popularity appears to have waned. Further, the advent of the fourth industrial revolution (4IR) or Industry 4.0 brings with it other integrated digital technologies, including the Internet of Things (IoT), Blockchain, and digital twin (DT), among others. DT offers functions like VM plus other benefits, including intelligent manufacturing, to revolutionize future manufacturing operations activities and predictive capability using real-time data.

  • digital technologies
  • virtual manufacturing
  • fourth industrial revolution

1. Introduction

Virtual manufacturing (VM) technology emerged in the 1980s as a revolutionary manufacturing strategy to transform conventional production and operations [1][2]. VM is a comprehensive, synthetic digital manufacturing environment that enhances all levels of decision and control of a manufacturing system [3][4][5]. The growing demand for greater productivity, lower production costs, and better product quality in the fiercely competitive global market was the primary impetus for the VM initiative [4][5]. In VM, sophisticated computer-based models, simulations, and data-driven analytics are created to optimize and streamline the entire product/service lifecycle by incorporating components of computer-aided design (CAD), computer-aided manufacturing (CAM), and simulation [5][6][7] Various functions are then performed to boost efficiency, sustainability, and innovation in the manufacturing industry, from original product design and prototyping to process optimization, resource planning, and quality assurance [8].
Additionally, VM encourages continual improvement by offering insightful information about the production process, allowing businesses to quickly adjust to market needs and keep a competitive advantage [8][9]. VM is anticipated to play an increasingly important role in determining the future of manufacturing and industry as fourth industrial revolution (4IR) technologies continue to develop [10]. Hence, VM offers significant benefits for product and service manufacturing throughout the entire product/service lifecycle [10][11]. The benefits include design and prototyping through visualization with the ability to refine techniques before committing to physical production [11]. A VM environment proffers process optimization through simulations and modeling with the ability to analyze different scenarios, identify bottlenecks, and fine-tune parameters to maximize efficiency, reduce waste, and improve overall productivity [12][13]. Thus, VM aids in the planning and management of resources effectively.
The emergence of 4IR brought with it a set of integrated technologies capable of revolutionizing the manufacturing industry and other sectors of the economy. As mentioned earlier, these integrated technologies cut across several fields, including computer technology and telecommunications, such as the Internet of Things, big data, virtual reality, simulation, and DT [10][14]. As technology evolves rapidly, 4IR will continue to impact different manufacturing areas in the coming years. 4IR, or Industry 4.0 (as it is also known), is characterized by the convergence of digital, physical, and biomedical technologies, transforming various industries and aspects of society [14]. Its key features include the widespread use of the Internet, popularly called the Internet of Things (IoT), connecting mobile devices for seamless communication and data exchange, and the integration of sensors and intelligent devices into various objects and machines to collect, share, and analyze data to enable automation, pattern recognition, and prediction capabilities [15][16][17]. Other characteristics of 4IR include advanced robotics and automation technologies that transform industries by enhancing productivity and efficiency, 3D printing technology that enables the creation of complex and customized objects, and augmented reality (AR) and virtual reality (VR) that provide immersive and interactive experiences [7][15][16][17]. Embracing and harnessing its potential while addressing its challenges is essential for ensuring a sustainable and inclusive future.

Virtual Manufacturing (VM) Technology

VM involves creating a digital or computer-based environment that mimics an actual system and simulating the different aspects of manufacturing operations and processes to optimize and improve efficiency, quality, and productivity before actual physical production occurs [2][5][6]. It entails the use of virtual reality or augmented reality and simulation technologies to model and evaluate the different components of manufacturing activities and processes, incorporating product design, digital prototyping, and production processes. VM also helps to ensure efficient resource utilization and quality control in manufacturing. Furthermore, VM helps to accelerate time to market by identifying and addressing potential issues virtually before they become costly problems in the physical world [4].
The development of VM came into existence following the emergence of allied manufacturing technologies, including CAD, CAM, simulation, and virtual reality (VR) [6][18]. Thus, VM integrates these technologies to present a virtual environment that enhances optimal product development. CAD and CAM are fundamental tools used to create detailed digital models of products, services, components, and manufacturing processes. CAD systems use mathematical and geometrical representations to describe the physical aspects of products or systems. CAM generates instructions for computers and robots to develop products. Generally, CAM converts CAD models into machine-specific and understandable code, allowing for automation and precision in the manufacturing process [6]. Mathematical models and simulations are utilized to analyze, predict, and correct the behavior of manufacturing processes by identifying potential issues, optimizing parameters, and evaluating different scenarios without the cost of building a physical prototype [18][19][20][21][22][23].

Fourth Industrial Revolution and Associated Technologies

The dramatic advances in computing power (hardware and software), Internet connectivity, and advanced communication technologies have led to the evolution of 4IR and associated technologies, including DT. A DT is a virtual replica of a physical manufacturing system. This virtual environment captures the manufacturing complex from design to behavior and real-time performance. Real-time monitoring, analysis, recommendation, and the physical system’s optimization are made possible using data collected from the virtual counterpart [24]. Interaction in the virtual environment of the DT is possible through virtual reality (VR) and augmented reality (AR) technologies. VR and AR enable virtual walks and work through manufacturing facilities with training simulations and visualization [14][20][23][24][25].
4IR is powered by various advances in mathematics and its applications in engineering, artificial intelligence (AI), data science, and other emerging fields using developments in computer science technologies [26][27]. Unlike the previous industrial revolution, 4IR is characterized by the convergence of data, intellectual discoveries, and technological advances rather than material discoveries [28]. This leads to transformative changes across industries and society. Information technology (IT) and the Internet of Things (IoT) comprise the reliable foundation on which 4IR lies. The rapid advancement of information technology, including the Internet, cloud computing, and the proliferation of connected devices, has enabled seamless communication, data sharing, and real-time connectivity between people, machines, and systems anywhere [29]. The natural consequence is the IoT. The analytic pillar of 4IR that fuels its running is data collection through sensors and smart devices implanted into objects and machines exchanging data. This interconnectedness and collection of data allow for intelligent data-driven decision making, automation, and optimization in various manufacturing sectors. The vast amounts of data (big data) collected require advanced analysis due to their volume, generation velocity, and variety. Machine learning and artificial intelligence models enable organizations to derive valuable insights from these data and make data-driven decisions [10][17].

2. Thematic Evolution of Virtual Manufacturing Literature (1983–2023)

Text analytics of author keywords and science mapping using the R-based Bibliometrix application highlight the trending themes in each segmented period (Figure 1).
Systems 11 00524 g003
Figure 1. Thematic evolution of virtual manufacturing (1983 to 2023).
The trend analysis highlights the origin and evolution of dominant VM research themes in three strata (1983–2003; 2004–2013; 2014–2023). The first segment covers the initial twenty years (1983–2003) of VM research. During this period, the total number of VM publications was 377, which are fewer than the number published in each of the two subsequent ten-year periods. This provides the rationale for the three-periods grouping of the VM thematic evolution (Figure 1). The next decade (2004–2013) accumulated 486 publications, and 2014–2023 had 357 publications per the cumulative total publication trend (Figure 2).
Figure 2. Virtual manufacturing scientific literature publications (1983–2023).
Some of the vital research themes in the first segment other than VM include “cellular manufacturing [30][31],” “digital factory [32],” “simulation [3][30],” “manufacturing systems [3][30],” “virtual machine tools [33],” “CAD/CAM [6][11],” and “virtual prototyping [32].” In addition to the themes covered in the first segment, new themes addressed in the second period include “3D simulation [34][35][36],” “production planning [37][38],” “augmented reality [39],” and “e-manufacturing [40].” Some new themes evolved in the third segment, including “virtual enterprise [41][42],” “digital design [43],” and “digital manufacturing [44].” The result shows the growth and continuous addition of new VM research terminologies that point to new digital technologies toward improving product and service manufacturing.
VM plays a significant role in 4IR by leveraging innovative and advanced digital technologies capable of transforming manufacturing processes [2][8]. During 2014–2023, which corresponds to the period of 4IR, scholars witnessed emerging concepts such as e-manufacturing, virtual machinery, and virtual enterprise [41][42]. E-manufacturing (electronic manufacturing) uses advanced digital technologies to optimize and streamline manufacturing processes. It integrates information technology, automation, data analytics, and connectivity to enhance manufacturing operations’ efficiency, flexibility, and productivity [40]. These digital technologies have revolutionized the manufacturing industry by improving efficiency, reducing waste, and enabling greater agility in response to changing market demands and other conditions. It empowers manufacturers to produce high-quality products at a lower cost while adapting to evolving technologies and customer preferences.
Virtual machinery simulates and mimics physical machinery, processes, or systems in a virtual or digital environment [45]. It involves using computer software and models to replicate real-world machinery’s behavior, functionality, and characteristics without the need for physical hardware. Overall, virtual machinery is a valuable tool in modern engineering and industry, allowing for the efficient development, operation, and maintenance of complex machinery and systems in a digital environment [41][45].
A virtual enterprise, also known as a virtual organization or networked enterprise, is a business model or organizational structure in which various geographically dispersed and independent entities collaborate and work together to achieve common goals, such as the delivery of products or services [41][46]. Usually, advanced information and communication technologies, including wireless networks, are tools that enhance business interconnectedness in a globalized world. Business objectives include leveraging technology and collaboration to lower costs and create value costs [42][46]. The use of these technologies has increased since the 4IR era and accelerated during the global health pandemic when virtual technologies became more pervasive [46][47].

This entry is adapted from the peer-reviewed paper 10.3390/systems11100524

References

  1. Savastano, M.; Amendola, C.; Bellini, F.; D’Ascenzo, F. Contextual impacts on industrial processes brought by the digital transformation of manufacturing: A systematic review. Sustainability 2019, 11, 891.
  2. Radharamanan, R. Virtual manufacturing: An emerging technology. In Proceedings of the 2002 American Society for Engineering Education Annual Conference & Exposition, Montreal, ON, Canada, 16–19 June 2002.
  3. Iwata, K.; Onosato, M.; Teramoto, K.; Osaki, S. Virtual manufacturing systems as advanced information infrastructure for integrating manufacturing resources and activities. CIRP Ann. 1997, 46, 335–338.
  4. Graf, M.; Härtel, S.; Binotsch, C.; Awiszus, B. Virtual Manufacturing as Tool for Material and Process Developments and Optimizations. J. Mech. Eng. 2018, 4, 33–59.
  5. Cheng TC, E.; Choy, P.W.; Wong, S.W. Virtual Manufacturing: Critical Capabilities and Their Organizational Performance Implications. IEEE Eng. Manag. Rev. 2022, 50, 103–114.
  6. Lim, K.T.; El-Mounayri, H. CAD/CAM-based geometric modeling simulation for virtual manufacturing. In Proceedings of the Electrical Insulation Conference and Electrical Manufacturing and Coil Winding Conference (Cat. No. 99CH37035), Cincinnati, OH, USA, 28 October 1999; pp. 547–551.
  7. Akpan, I.J.; Shanker, M. The confirmed realities and myths about the benefits and costs of 3D visualization and virtual reality in discrete event modeling and simulation: A descriptive meta-analysis of evidence from research and practice. Comput. Ind. Eng. 2017, 112, 197–211.
  8. Souza, M.C.F.; Sacco, M.; Porto, A.J.V. Virtual manufacturing as a way for the factory of the future. J. Intell. Manuf. 2006, 17, 725–735.
  9. Westkämper, E.; von Briel, R. Continuous improvement and participative factory planning by computer systems. CIRP Ann. 2001, 50, 347–352.
  10. Kumar, K.; Zindani, D.; Davim, J.P. Industry 4.0: Developments towards the Fourth Industrial Revolution; Springer: Cham, Switzerland, 2019.
  11. Yuan, Q.K.; Luo, S.M.; Tang, W.Y. A collaborative design frame based on virtual prototypes and virtual manufacturing. In Proceedings of the 11th IEEE International Conference on Computer-Aided Design and Computer Graphics, Huangshan City, China, 19–21 August 2009; pp. 568–571.
  12. Zhou, J.; Liu, Z.Q.; Huang, P.L.; Ai, X.; Deng, J.X. Simulation Diagnosis for the Bottleneck of Production Lines and Its Application. Mater. Sci. Forum 2004, 471, 563–567.
  13. Akpan, I.J.; Brooks, R.J. Experimental evaluation of user performance on two-dimensional and three-dimensional perspective displays in discrete-event simulation. Decis. Support Syst. 2014, 64, 14–30.
  14. Li, X.; Wang, L.; Zhu, C.; Liu, Z. Framework for manufacturing-tasks semantic modelling and manufacturing-resource recommendation for digital twin shop-floor. J. Manuf. Syst. 2021, 58, 281–292.
  15. Gilchrist, A. Introducing Industry 4.0. In Industry 4.0; Apress: Berkeley, CA, USA, 2016.
  16. Azarian, M.; Yu, H.; Solvang, W.D. Integrating additive manufacturing into a virtual Industry 4.0 factory. In Advanced Manufacturing and Automation; Springer: Singapore, 2021; Volume X10, pp. 587–594.
  17. Lind, A.; Elango, V.; Hanson, L.; Högberg, D.; Lämkull, D.; Mårtensson, P.; Syberfeldt, A. Virtual-Simulation-Based Multi-Objective Optimization of an Assembly Station in a Battery Production Factory. Systems 2023, 11, 395.
  18. Onosato, M.; Iwata, K. Development of a virtual manufacturing system by integrating product models and factory models. CIRP Ann. 1993, 42, 475–478.
  19. Wang, G.G. Definition review of virtual prototyping. J. Comput. Inf. Sci. Eng. 2002, 2, 232–236.
  20. Mujber, T.S.; Szecsi, T.; Hashmi, M.S. Virtual reality applications in manufacturing process simulation. J. Mater. Process. Technol. 2004, 155, 1834–1838.
  21. Williams, J.R.; Pentland, A.P. Superquadrics and modal dynamics for discrete elements in interactive design. Eng. Comput. 1992, 9, 115–127.
  22. Choi, S.; Jung, K.; Noh, S.D. Virtual reality applications in manufacturing industries: Past research, present findings, and future directions. Concurr. Eng. 2015, 23, 40–63.
  23. Maturana, F.; Shen, W.; Norrie, D.H. MetaMorph: An adaptive agent-based architecture for intelligent manufacturing. Int. J. Prod. Res. 1999, 37, 2159–2173.
  24. Alexopoulos, K.; Nikolakis, N.; Chryssolouris, G. Digital twin-driven supervised machine learning for the development of artificial intelligence applications in manufacturing. Int. J. Comput. Integr. Manuf. 2020, 33, 429–439.
  25. Wei, X.; Wu, H.; Yang, Z.; Han, C.; Xu, B. Simulation of manufacturing scenarios’ ambidexterity green technological innovation driven by inter-firm social networks: Based on a multi-objective model. Systems 2023, 11, 39.
  26. Bühler, M.M.; Jelinek, T.; Nübel, K. Training and preparing tomorrow’s workforce for the fourth industrial revolution. Educ. Sci. 2022, 12, 782.
  27. Oladele, J.I.; Ndlovu, M.; Ayanwale, M.A. Computer Adaptive-Based Learning and Assessment for Enhancing STEM Education in Africa: A Fourth Industrial Revolution Possibility. In Mathematics Education in Africa: The Fourth Industrial Revolution; Springer International Publishing: Cham, Switzerland, 2022; pp. 131–144.
  28. Frederico, G.F. From supply chain 4.0 to supply chain 5.0: Findings from a systematic literature review and research directions. Logistics 2021, 5, 49.
  29. Munirathinam, S. Industry 4.0: Industrial internet of things (IIOT). Adv. Comput. 2020, 117, 129–164.
  30. Mak, K.L.; Wang, X.X. Production scheduling and cell formation for virtual cellular manufacturing systems. Int. J. Adv. Manuf. Technol. 2002, 20, 144–152.
  31. Kannan, V.R. A simulation analysis of the impact of family configuration on virtual cellular manufacturing. Prod. Plan. Control 1997, 8, 14–24.
  32. da Silva, G.C.; Kaminski, P.C. Application of digital factory concepts to optimise and integrate inventories in automotive pre-assembly areas. Int. J. Comput. Integr. Manuf. 2015, 28, 607–615.
  33. Li, Z.F.; Xu, J. Virtual manufacturing technology and application over tooling machines. Adv. Mater. Res. 2012, 588, 1748–1750.
  34. Akpan, I.J.; Brooks, R.J. Users’ perceptions of the relative costs and benefits of 2D and 3D visual displays in discrete-event simulation. Simulation 2012, 88, 464–480.
  35. Qin, S.F.; Harrison, R.; West, A.A.; Wright, D.K. Development of a novel 3D simulation modelling system for distributed manufacturing. Comput. Ind. 2004, 54, 69–81.
  36. Shen, Z.; Shah, J.J.; Davidson, J.K. A complete variation algorithm for slot and tab features for 3D simulation-based tolerance analysis. In Proceedings of the International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Long Beach, CA, USA, 24–28 September 2005; Volume 4739, pp. 587–597.
  37. Karageorgos, A.; Mehandjiev, N.; Weichhart, G.; Hämmerle, A. Agent-based optimisation of logistics and production planning. Eng. Appl. Artif. Intell. 2003, 16, 335–348.
  38. Tu, Y. Production planning and control in a virtual one-of-a-kind production company. Comput. Ind. 1997, 34, 271–283.
  39. Novak-Marcincin, J.; Novakova-Marcincinova, L. Application of augmented reality technologies in virtual manufacturing. Acad. J. Manuf. Eng. 2013, 11, 48–51.
  40. Nyanga, L.; Van der Merwe, A.F.; Matope, S.; Dewa, M.T. A web-based manufacturability agent framework for an E-manufacturing system. Procedia CIRP 2015, 28, 167–172.
  41. Wen, X.L. Research on the destructive Leadership in Virtual Enterprises. Adv. Mater. Res. 2013, 712, 3199–3202.
  42. Wang, H. Research on the resource sharing among hierarchically distributed virtual enterprises based on grid system. Open Cybern. Syst. J. 2014, 8, 385–392.
  43. Yang, J.; Kim, E.; Hur, M.; Cho, S.; Han, M.; Seo, I. Knowledge extraction and visualization of digital design process. Expert Syst. Appl. 2018, 92, 206–215.
  44. Lambiase, F.; Lambiase, A. An integrated approach to the analysis of automotive assembly activities using digital manufacturing tools. Int. J. Internet Manuf. Serv. 2008, 1, 160–175.
  45. Kadir, A.A.; Xu, X.; Hämmerle, E. Virtual machine tools and virtual machining—A technological review. Robot. Comput.-Integr. Manuf. 2011, 27, 494–508.
  46. Akpan, I.J.; Effiom, L.; Akpanobong, A.C. Towards developing a knowledge base for small business survival techniques during COVID-19 and sustainable growth strategies for the post-pandemic era. J. Small Bus. Entrep. 2023, 1–23.
  47. Anthony Jnr, B. A developed distributed ledger technology architectural layer framework for decentralized governance implementation in virtual enterprise. Inf. Syst. e-Bus. Manag. 2023, 21, 437–470.
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.
This entry is offline, you can click here to edit this entry!
Video Production Service
Feedback