1. Introduction

With the use of cutting-edge computer technology, virtual reality (VR) may simulate mechanics in a real or imagined world and provide users with a variety of intuitive feelings. [1] found that virtual reality (VR) has no one accepted definition; instead, definitions vary depending on the intended use and setup [2], [3]. Virtual reality (VR) may be thought of as a logical progression of conventional computer graphics to 3D displays with sophisticated inputs and outputs [4]. Its study is broken down into two categories: software and hardware [1], [5]. Early VR equipment was incredibly bulky, costly, and ineffective. After decades of development, VR systems as a whole can provide consumers with a higher degree of spatial immersion; technology has shrunk in size and become more affordable; and software has improved.

Virtual reality (VR) applications provide users with more than just realistic sight; they may also sense touch, hear, and even interact with virtual objects. With these significant advancements, virtual reality has seen recent growth surges in a number of sectors. It has been successful in drawing attention from both academics and industry. Researchers and practitioners are very interested in exploring the transformation of industries brought about by the fourth industrial revolution around the world [6]. The German government, academic institutions, and private businesses first introduced the idea of "industry 4.0," which seeks to leverage cutting-edge concepts and technology to move industrial manufacture into a new era known as "smart manufacturing" [7], [8]. Its goal is to incorporate cutting-edge technologies to raise manufacturing industry standards for efficacy, productivity, and quality [9]. Virtual reality (VR) is regarded as one of Industry 4.0's cutting-edge front-end technologies for enabling smart working [8]. The same is true for manufacturing.

According to [10], the five categories of VR technologies are olfactory/taste, haptic, visual, bio-signals, and auditory. Therefore, visual is further divided into augmented reality, immersion-typed, and desktop-typed [11]. In addition to prototypes, authoring technology includes the use of “computer-aided design,” “virtual reality modelling language,” and equivalents [10], [12]. In this research, AR and VR will have a nearly closer meaning because the search query considered them synonyms for virtual environments.

The manufacturing sector has seen substantial transformations ever since the onset of the initial technological revolution, introducing steam power and mechanized production [13]. Electricity and assembly lines were introduced into industries during the second technological revolution. The emergence of automation in the 1970s sparked a third technological revolution in industries [14]. “Smart production” [15] is made possible by the integration of digital technology into the manufacturing setting through initiatives like Industry 4.0 [16]. The realization of smart capabilities in the future manufacturing industry relies significantly on digital technologies. Digital twins [17], augmented reality (AR) [13], cloud computing [18], IoT [19], predictive maintenance [20], big data [21], and virtual reality [10] are a few examples of digital technologies.

The use of digital technologies has led to previously unheard-of amounts of data collection and information generation. Building cyber-physical production systems that seamlessly integrate the digital and physical worlds is the goal of Industry 4.0 [22]. By doing so, manufacturing will become more intelligent and consequently more flexible, adaptable, and autonomous [23]. Humans continue to be crucial to manufacturing processes, even with this emphasis on technology [24], [25]. AR is used to enable real-time, contextual human access to massive amounts of data produced by CPPS [22]. Because AR supports people in an intelligent manufacturing environment, enabling this Industry 4.0 manufacturing approach with a focus on human-centricity is crucial [26].

According to [27], the European Union has identified augmented reality (AR) as a key technology that will propel the growth of smart manufacturing. Researchers concentrate on augmented reality (AR) to accomplish the goal of facilitating human-digital data-based production system collaboration and interaction [14]. Although many technologies contribute to the fourth industrial revolution [27], augmented reality (AR) is the only one that focuses on enhancing human-machine and, consequently, human-intelligent manufacturing system interaction. Thus, it is essential to comprehend the present state of augmented reality in industrial research. In this instance, the bibliometric examination is required in order to determine the areas of structure, growth, and present knowledge based on the connection between virtual reality and the manufacturing industry that require more investigation or evaluation.

[28] assert that bibliometric analysis serves as an effective method for gauging the impact of publications within the scientific community. This approach is characterized as a statistical assessment of published book chapters, scientific articles, or books. Identifying key authors, core research, and their interconnections necessitates a systematic computer-assisted review process that surveys all publications within a specific topic or field [29], [30].

Bibliometric analyses have the capability to statistically portray official intellectual frameworks, revealing collaboratives, nations, co-citation patterns, sources, and citations, as well as keywords and correlations among fields of study. This is particularly evident when combined with social network analysis techniques, showcasing clusters and networks. Consequently, conducting a thorough analysis of the research trajectory contributes to a more profound comprehension of the subject.

[31]’s paper was about the role of virtual reality on manufacturing industry limited to only industry 4.0. This bibliometric manuscript studies the connection between virtual reality and the manufacturing industry as a whole, and not only 4.0; this leads to a wider scope compared to [31]’s research. Furthermore, the article used the Web of Sciences database, so since this study uses the Scopus database, it might be regarded as widening and supplementing the literature.

[32] used mostly content analysis to study the topic and a little bibliometric method, the result of which differs from that of bibliometric analysis. For instance, using correlation to look for a gap is quite different from the gap given by the keywords, and the same is true for the redundant topics. Furthermore, the authors did the study in 2015 which is due to time variant and speed of technological change, there might be a difference in coverage. These are the differences between the two.

Apart from the above gaps, this study was to correct the limitations reported by [33]: “the use of two databases for publication selection (Scopus, Web of Science)." This study uses only Scopus for data sampling and collection. Also, [33] was done for the period 2012–2022, which is too narrow as the domain started publishing in 1990s. To fill this gap, we did this study.

Therefore, the study's methodology for concentrating on the connection between virtual reality and manufacturing industry trends is still unknown. It is also uncertain who the main writers and contributors are in the most well-known publications on the connection between virtual reality and the manufacturing industry, as well as the research that is quoted the most. The objectives of this bibliometric review are to enhance scholars' comprehension of historical, current, and prospective trends and to propose potential avenues for future studies. Consequently, an assessment of the connection between virtual reality and manufacturing industry research was conducted using that science mapping methodology, concentrating primarily on these study questions:

RQ1: What is the trend in the growth of research on the connection between virtual reality and the manufacturing industry?

RQ2: Which countries, writers, sources, and publications have contributed the most to the understanding of the connection between virtual reality and the manufacturing industry?

RQ3: What is the scope of the knowledge framework for the literature on the connection between virtual reality and the manufacturing industry?

RQ4: In the connection between virtual reality and manufacturing industry research, what are the research gaps?

The goal of this research review is to use bibliometric analysis, the most effective way for analyzing the conceptual structure of the field, to develop a sustainable knowledge base by examining the corpus of research on the connection between virtual reality and the manufacturing industry [34]. Using bibliometric approaches, the study examined a dataset of 2,037 publications that were indexed by Scopus. Using descriptive statistics, trends in the composition and growth of the connection between virtual reality and manufacturing industry literature were recorded. Citations, co-authorship, co-citation, and co-occurrence analysis were used to find authorship, documents, and subject patterns [35].

2. Methodology

Using techniques for example co-occurrence analysis, citation analysis, co-authorship analysis, and co-citation analysis, the study performed a bibliometric analysis of the association between virtual reality and manufacturing industry literature [36]. “Several software tools are commonly used for bibliometric analysis. Some of the most prominent ones include VOSviewer, Bibliometrix (an R package), CiteSpace, and BibExcel, among others.” The literature uses the VOSviewer package to do bibliometric analysis [36]. The versatility of the VOSviewer software is the reason to use it. Contrary to most applications, VOSviewer provides a sizable graphical illustration of bibliometric networks [37]. First, on December 22, 2023, we looked over the literature on the connection between virtual reality and the manufacturing industry to obtain an up-to-date overview of the field and compile a list of frequently used terms. We established our inclusion criteria following the PRISMA procedure before commencing the data gathering process [38], [39].

2.1. Locating Resources for the Review

Given that empirical investigations have demonstrated Scopus to provide more extensive coverage of sources in the field of social sciences compared to the Web of Science, we opted to use the Scopus index as the data source for extracting papers [40]. According to [41], there is a possibility to argue that, acknowledging that the narrower coverage of Web of Science results in a database with higher-quality sources, it is important to note that this observation is subject to confirmation through objective verification, particularly in the context of specific topics. As a result, we looked to a prior study by [42] and found that there was a significant correlation between the citations and papers in Scopus and Web of Science.

2.2. Search Delimiting Criteria

Using the anticipated search string, we conducted our first search on December 22, 2023, on the "Scopus" database. We defined a set of inclusion and exclusion variables to limit our findings. After going through the four stages, we were able to extract the pertinent articles for our evaluation from the initial 4,776 articles that we found (Fig. 1). We were left with 4,373 articles after applying the topic area delimitation criteria, which included only papers from the computer science, management, engineering, business, accounting, econometrics, economics, finance, and social sciences domains. To obtain articles with just pertinent terms for the connection between virtual reality and the manufacturing industry, we employed keywords as inclusion and exclusion criteria. After removing 2,281 articles, 2,092 articles were left. We eliminated 55 papers written in other languages because we only required English-language publications. This left us with 2,037 final articles for our evaluation and bibliographic analysis.

In this instance, we entered the request in the Scopus Database by means of the "TITLE-ABS-KEY" tool [36]. We gathered all the material that was subjected to a peer review process by limiting the search to English-language publications in the "final papers" category that were released no later than December 22, 2023.

The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach helped as the search criteria for the documents [39]. A Scopus article title, abstract, and keyword search were conducted using the following search terms: (“simulated reality” OR “augmented reality” OR “virtual reality” OR “artificial reality”) AND (“manufacturing industry” OR “manufacturing sector” OR “industrial sector"), and 4,776 documents were located during this search. 2,739 papers were eliminated following screening and eligibility verification because they had undesired specifications or were not sufficiently significant (refer to Fig. 1). In the finished database, 2,037 documents were included.

 

Fig. 1. The PRISMA Flow chart Showing Systematic Sampling [38]

3. Results and Discussion

2,037 documents, including citations, affiliations, authors, and titles, were stored for future analysis. Examples of advanced bibliometric analyses include co-citation and citation analysis, along with the "visualization of similarities" through co-citation and co-occurrence analysis. [36], [43]. Tableau, the VOSviewer package, and Excel were among the Scopus analysis tools used in this research [43]. This section unveils the results derived from the bibliometric examination of the literature on virtual reality. The responses to the research questions are presented in the following sequence:

3.1. Descriptive Trends in the Virtual Reality Knowledge Base

An essay written by Theasby P. J. published in 1992 titled “Virtues of Virtual Reality” served as the review's initial source. It wasn't until the year 1995 that scholarly interest really took off, as the number of papers increased annually. To further analyse the growth of the keywords, the timeframe was to be grouped into smaller time intervals. The study examined the temporal evolution of keywords by considering their timelines and normalising the frequencies by number of keywords in each time sub-period, as documented as per [44]. The evolution of keywords is illustrated for the entire period and each of the three sub-periods in Table 1. Regarding the initial phase, sub-period one (1992–2017) saw the publication of an average of just 17 publications annually (Fig. 2). The four terms that showed the largest co-occurrence, with more than 5 percent of the overall occurrences, were virtual reality, process simulation, augmented reality, and manufacturing industries.

Referred to as the "take-off phase," the second sub-period, which runs from 2018 to 2020, was marked by a progressive increase in the quantity of articles. Every year, on average, over 188 papers were published. The seven most common terms throughout the sub-period were “Industry 4.0 technologies,” “virtual reality,” “augmented reality,” “manufacturing industries,” “industrial internet of things,” “process simulation,” and “smart manufacturing systems”; each accounted for more than 5 percent of all terms used. The evolution of the industrial internet of things, smart manufacturing systems, digital twins, cross-reality, smart technologies, and assembly assistance systems took place throughout this period. However, the keywords “cross reality,” “smart technologies,” and “assembly assistance systems” lasted for only this single period and disappeared. The use of specific co-words like “virtual reality” and “augmented reality” might be the reasons for the disappearance of cross-reality. During this period, the keyword “industrial internet of things” became more and more popular among scholars as the foundation for virtual environments.

Table 1 Keywords’ growth movement in the connection between virtual reality and manufacturing industry

The third sub-period, which runs from 2021 to 2023, is the present phase. This sub-period contains an average of about 340 articles per year, and this phase saw the beginning of the significant increase. “Industry 4.0 technologies,” “augmented reality,” and “virtual reality” serve as the main subjects. They can be viewed as focused terms that become noticeable as top keywords over time. The terms "virtual reality," "augmented reality," "process simulation," “manufacturing industries," “autonomous robots," “3D technologies," “industry 4.0 technologies," “cognitive ergonomics," and “cyber-physical system" are the most frequently used keywords and are also important issues concerning the virtual reality domain. The terms appeared most frequently throughout all sub-periods (1992–2023).

When it reached a maximum of 378 in 2022, it had the biggest surge. But in 2023, it had fallen to 342 (see Fig. 2). The examination of the co-occurrence keywords suggests artificial intelligence and human-robot interaction as newly developed keywords during the third sub-period meaning that, these technologies started being used in this field in this period.

 

Fig. 2. Research growth in the connection between virtual reality and manufacturing industry

 

3.1.1. The Growing Trend of the Five Most Occurring Topics in Virtual Reality

The evolution of the most important subjects that have made the biggest contributions to the body of knowledge in earnings management is better understood by us and the readers thanks to the growth history. As a result, virtual reality experienced the highest growth rate of any occurrence in the first sub-period, at about 37 percent. The growth rate dropped to 17.4 percent in the period between 2018 and 2020; thereafter, it fell to 15.3 percent in the period between 2021 and 2023. Overall, the keyword had 20 percent growth(Table 1). Its high growth can be attributed to the findings by [45] that virtual reality enhances design review teams' communication by lessening the exclusion of professional groups; it may also speed up the evaluation process and solve user isolation difficulties [46]. However, problems occur with the interactive aspect of VR movement, necessitating the requirement for a "freeze" feature. Lack of data transfer standards prevents participants from requesting real-time computer-aided design changes in virtual reality [46]. [45] highlighted that industry standards were not supported by current VR development software, which highlights the need for further advancements. This was the stimulus for researchers to keep studying the topic, and so it became a highly used keyword.

Being among the top two concepts in virtual reality, augmented reality is a reasonably important concept that appears in all sub-periods and is reported by the literature to have the most impact. AR having 9.3 percent growth in the period between 1992 and 2020 was the second-highest percentage of all occurrences. Its growth rate rose to 14.4 percent in the period between 2018 and 2020. As a result, in the period between 2021 and 2023, it accounted for 17.1 percent of all co-occurrences. As a whole, 17.6 percent of all instances had an average growth rate for augmented reality (Table 1). The idea's growing appeal is a result of its possible application in the manufacturing industry [47]. According to [13], augmented reality plays a crucial role in providing real-time contextual accessibility to the vast amount of data generated by cyber-physical production systems for humans [22], and it is pivotal in implementing a human-centred approach to Industry 4.0 manufacturing [48], actively supporting intelligent manufacturing environments. Also recognized by the European Union as her key technology driving smart factories' development [13], as cited in Davies’s study done in 2015, AR is instrumental in fostering cooperation and engagement among individuals and digital data-based manufacturing systems [14].

The amount of literature on “Industry 4.0 technologies” has been growing gradually over the years, growing at a pace of about 3.7 percent in the period between 1992 and 2017. In the period between 2018 and 2020, this rate increased to approximately 17.6 percent. Thereafter, the growth rate increased to 17.9 percent in the period between 2021 and 2023. The field of study is still relatively new, despite being the third fastest-growing term (Table 1), with 17.4 percent of all incidences. This sharp growth might indicate that there is enough interest in the subject among researchers. Chiarello et al. [6] uncovered that the realm of Industry 4.0 technology is not novel; however, it is exceptionally diverse, encompassing over 30 distinct technological domains [9]. Consequently, numerous stakeholders find themselves uneasy, lacking mastery over the entire spectrum of technologies, and experiencing challenges in communication with other domains [6]. This made researchers keep on studying the topic, which made the keyword appear at the top of the sub-periods.

In the first period, the word "smart manufacturing systems" didn’t appear in the top fifteen keywords. Nevertheless, its growth rate accounted for 5.3 percent of the co-occurrences in the period between 2018 and 2020 and a minor increase to 7.2 percent in the period between 2021 and 2023 (Table 1). Nonetheless, the closeness of smart manufacturing systems to other technologies [49] made it frequently mentioned in the period.

The incidence rate of the keyword "manufacturing industries" had inconsistence growth rates, as shown by growth rates of 5.6 percent in the period between 1992 and 2017, 5.9 percent in the period between 2018 and 2020, and dropped to 4.6 percent in the period between 2021 and 2023. With an average growth rate of 5.4 percent, this gain is gradually moving towards the 10 most frequently used terms. The manufacturing industries being the key term in the research title, all the papers were done in the field, and the technologies were supporting tools for sustainable development.

3.2. The Scope of the Most Productive Stakeholders in the Virtual Reality Literature

Gaining insight into the present state of research on virtual reality and the manufacturing industry can be facilitated by being aware of the authors and materials that contribute the most to the knowledge base. Additionally, it can assist in locating possible sources for fresh insights and investigations that might result in more advancements in the area. Moreover, it can give scholars a sense of which nations, publications, writers, and papers are the most significant and ought to be referenced for further details, as follows:

3.2.1. Productive Countries in the Virtual Reality Literature

Researchers can identify the active nations, comprehend contemporary patterns in research, on virtual reality and the manufacturing industry, and obtain insight into the standards being established for domain procedures by having knowledge of the countries that are very productive in this area. In addition, an analysis of the writers' geographic locations was carried out to determine the areas of academia where virtual reality and manufacturing industry research have received academic attention.

The study examined the authors' geographical locations to ascertain the areas of scholarly interest that have been concentrated on the connection between virtual reality and the manufacturing industry. The fact that this corpus of material was written in 114 different nations shows how popular the topic is around the world (Fig. 3). China (288), the United States (216), Italy (206), Germany (191), the United Kingdom (173), India (129), France (97), Sweden (91), Spain (81), Portugal (71), Brazil (61), Australia (53), Canada (52), Finland (52), and Greece (47) were the countries with the highest concentration of authorship, nevertheless. Researchers having ties to these fifteen nations provided more than half of the research on the connection between virtual reality and the manufacturing industry gathered for this review (Table 2).

Table 2 The Most Productive Countries by Number of Documents Published

 

Fig. 3. Globally distribution of research in virtual reality literature

 

Additionally, as Table 3 illustrates, of the top fifteen nations by citation count, researchers from the UK (10,070), the USA (8,465), Italy (7,933), China (5,072), Germany (4,892), Spain (4,151), France (3,799), India (3,391), New Zealand (3,266), Brazil (2,913), Iran (2,374), Australia (2,366), Portugal (1,958), Mexico (1,893), and Turkey (1,836) contributed more than half of the virtual reality and manufacturing industry citations examined in this review.

Table 3 The Most Prolific Countries by number of citations

As a result, the aforementioned nations made up the majority of contributors, wielding great influence in the field and greatly influencing academics through their research. In this instance, developed nations receive the majority of the research on virtual reality and the manufacturing industry, leaving developing nations uninformed (Tables 2, 3, and Fig. 3).

3.2.2. Examination of the Primary Contributing Journals

To stay abreast of the latest developments in research, as well as to discern the journals that are likely to accept their manuscripts and align with their research topics, researchers should acquaint themselves with the most influential journals in virtual reality literature. The 2,037 virtual reality papers in this instance were dispersed throughout 848 sources. However, the majority of those sources—55 percent—had several publications. The top fifteen journals, indicated in Table 4, have more than 33 percent of the corpus. With fifty-eight papers, the “lecture notes in computer science” journal was the most productive. However, 42,879 citations were shared by the 848 sources. Of all the sources, over twenty percent (not attached) had no citations, while the fifteen most productive provided over sixty percent of the citations (Table 4). With 3,145 citations from 42 publications, the “Journal of Manufacturing Systems” was the most prolific source; Table 4 lists the statistics for the other prolific sources.

Table 4 The most Productive sources in virtual reality

3.2.3. Analysis of Influential Authors in Virtual Reality Literature

The most prominent researchers in the connection between virtual reality and manufacturing industry literature are indicated in Table 5. Xu Xun had 2987 citations; Ghobakhloo, Morteza (1771); Zhong, Ray (1732); Klotz, Eberhard (1710); and Newman, Stephen (1710), among others exhibited in Table 5, are the most cited authors and, therefore, the most prolific ones. The authors' citations impact significantly and are realistic, as Table 5 demonstrates. The Scopus h-index, on the other hand, takes into account an author's entire body of scholarly work, which goes beyond the subject of the connection between virtual reality and the manufacturing industry [36]; thus, we haven't taken it into consideration. As a result, Table 5's citations are solely derived from the publications written by each author in our review domain.

Table 5 The most influential authors by Number of Citations

3.2.4. Analysis of Influential Documents in Virtual Reality Literature

Based on all Scopus citations, Table 6 displays the papers that have been cited the most in virtual reality and manufacturing industry research. This examination aimed to assess the impact of researchers' contributions to the domain. Fifteen papers contained more than 13,103 citations. Considering how recent the connection between virtual reality and manufacturing industry literature is, these citations fall within a fair range. Therefore, the article [50], with 1,710 citations, stands out as the most frequently cited in this domain. It is among the top-cited articles displayed in Table 6. However, the most relevant and influential documents were not among the most cited. So, Table 7 displays the most relevant and influential articles on the influence of virtual reality on the manufacturing industry (discussed), along with their respective citation counts.

Table 6 The most prolific documents in Virtual Reality Literature

Table 7 The prolific documents most relevant to virtual reality research (discussed)

[51] did an inclusive survey of augmented reality in “manufacturing activities,” such as assembly, maintenance, product development, and training. It assesses the software systems and hardware utilized in augmented reality, examines the primary research endeavors, and discusses the challenges associated with implementing AR in “manufacturing” and the current and future trends of AR technology. It also discusses some relevant issues, such as tracking, registration, view management, and user interface design, that affect the performance and usability of AR systems. The paper aims to provide useful insights and references for researchers, students, and engineers who are interested in using AR as a tool in manufacturing research and practice.

[52]’s study explored the applications of AR and VR in the AEC (architecture, engineering, and construction) industries. It also suggests a research program to close current gaps in necessary skills. 54 experts from 36 organisations participated in exploratory workshops, completed questionnaires, and reviewed relevant literature to gather data for the project. Design support, stakeholder engagement, design review, operations and management support, training, and construction support constitute the six use-cases for augmented reality and virtual reality in the AEC sectors that are defined in the study. For every use case, the study highlights the primary advantages, difficulties, and research areas. It also offers a research roadmap to direct future investigations. The purpose of the study is to educate scholars and practitioners as well as their potential in the future.

[53] presented a broad overview of virtual prototyping methods for mechanical product development, including the primary advantages, obstacles, and areas of ongoing research in this domain. Along with providing sources for more reading, the document also provides a summary of the many VP techniques, including fit and interference, testing and verification, manufacturing evaluation, and human factor analysis. The goal of the paper is to provide potential SME users with a thorough understanding of VP so they can choose VP technology wisely.

[54] presented a methodological paper that takes into account eighteen parameters that represent the unique needs and difficulties of the industrial work floor while assessing and choosing augmented reality smart glasses (ARSG). The procedure is also used in the article to evaluate twelve products that are currently on the market and suggest which one is ideal for the shop floor. In addition, the paper lists five areas that require deeper investigation in order to guarantee the successful application of ARSG in the smart factory.

[55] introduced a novel paradigm known as the CPMC (Cyber-Physical Manufacturing Cloud), enabling the direct operation and controlling of machines within a “manufacturing cloud” through the Internet. This is achieved by seamlessly integrating “cloud computing” and “service-oriented technologies” into production processes. The study puts forth a virtualization approach for manufacturing resources, outlines a scalable and serviceable tiered style for the “CPMC,” and establishes communication mechanisms across its layers using protocols such as TCP/IP, REST, and MTConnect. Furthermore, the study implements and assesses a fully functional “testbed” of CPMC grounded on the proposed style, which also shows the technology's viability and efficiency in a number of production scenarios.

3.3. Intellectual Structure of Virtual Reality Knowledge Base

Scientists utilizing scientific mapping review techniques have explored the "intellectual structure" across various academic disciplines [56]. The term "intellectual structure" denotes the fundamental theoretical and empirical research trajectories that define a particular field of study. Employing author co-citation analysis, a network map in VOSviewer was generated to illustrate the intellectual organization within the knowledge bases of virtual reality and the manufacturing industry.

A co-citation examination was used to examine how frequently writers were mentioned jointly in the reference lists of the 2,037 papers. Consequently, compared to Scopus citation, co-citation analysis explains a substantially greater corpus of literature.

Researchers utilizing co-citation analysis argue that authors sharing a similar research perspective are those frequently co-cited by their peers [41]. Furthermore, through the examination of "author co-citations," the VOSviewer software can generate a network map that visually represents shared attributes among the authors cited in our leveraged database [36], [43].

When VOSviewer was used with a minimum of 100 author co-citations as the criterion, 174 academicians (Fig. 4) were shown on the co-citation network. The larger nodes represent important researchers based on the number of co-citations. Scholars are divided into research topics by vibrant clusters based on co-citation links. 3D technologies and augmented reality in manufacturing (green cluster), industry 4.0 technologies and virtual reality (red cluster), and smart manufacturing (blue cluster) comprise the intellectual structure of the connection between virtual reality and manufacturing industry literature.

 

Fig. 4. Network map for the connection between virtual reality and manufacturing industry

 

With 734 co-citations, Tao, F., an expert on industry 4.0 technologies and virtual reality (the red cluster), has the greatest research field. Wang I., Xu X., and Liu, Y., researchers on the same topic, have 721, 581, and 522 co-citations, respectively. With 621, 618, 568, and 564 co-citations, the 3D technologies and augmented reality in manufacturing experts Nee A. Y. C., Ong S. K., Mourtzis D., and Wang X., respectively (the green cluster), marked the next cluster.

Table 8 The Most Collaborative Authors by Number of Co-citations

Then, with 485, 337, and 333 co-citations, Lee J., Li D., and Wang S., respectively, were specialists in smart manufacturing (the blue cluster). The lists of the top 15 writers are displayed in Table 8.

3.4. Topical Concentrations in Virtual Reality Knowledge Base

Using keyword analysis, the themes discussed in the connection between virtual reality and manufacturing industry literature were examined. Using VOSviewer, we first determined which terms were most frequently used. The terms "virtual reality" with 513 occurrences, "augmented reality" (455), “industry 4.0 technologies” (451), “smart manufacturing systems” (164), “manufacturing industries” (141), “industrial internet of things” (139), "process simulation" (137), and “digital twins” (104), among others, are the most referenced keywords in all periods and are also important issues concerning the virtual reality domain. The writers’ co-citations, which indicated that all of the clusters were related to the connection between virtual reality and the manufacturing industry and that the word therefore became significant, are supported by this pattern of results (Fig. 5).

Subsequently, employing a threshold of a minimum of 5 co-occurrences, we created a "chronological keyword map" (Fig. 5) in VOSviewer [43]. The chronological co-word analysis scrutinizes the distribution of keywords over time concerning the paper publication date. The darker nodes reflect themes that were common in the past, whereas the yellower or lighter-tinted bubbles reflect the most current issues of interest to researchers in this discipline. This map can be interpreted by looking at the node size (occurrence), colour (recentness), and position (connection to other themes).

At the core of the map, the topic of "virtual reality" has the greatest connections to other subjects and is currently of interest. This conclusion is consistent with the discussion around the conceptual framework of the knowledge base, wherein the five study fields gave significant weight to the connection between virtual reality and the manufacturing industry.

An author keyword examination was used to decide the future course of the virtual reality study. After extracting the author keywords from our database of 2,037 relevant articles, an author keyword network was constructed using the VOSviewer programme. To get logical data, we imposed a condition of at least five co-occurrences of keywords. Of the 4,238 key words, 165 satisfied our requirements. It was found that the most common keyword, "virtual reality," appeared 513 times, so it is the largest node in the network, based on the map (Fig. 5).

 

Fig. 5. Topical concentration map for research in virtual reality literature

 

Keywords that have similar colours together signify that they belong to the same group. This suggests that different aspects of "virtual reality" have been addressed. The examination of the keyword network produced several conclusions. In summary, it first showed that augmented reality, manufacturing industries, smart manufacturing systems, artificial intelligence, industry 4.0 technologies, 3D technologies, industrial internet of things, and process simulation are the ones that are generally studied in terms of the studied domain.

4. Conclusion, Research Gap, and Limitations

4.1. Conclusion

The scope of the connection between virtual reality and manufacturing industry literature is demonstrated by this review of works. Despite the fact that the first pertinent academic paper published on the Scopus database was in 1992, the majority was published in the last decade. This is because virtual reality is perceived as a still-new research area, despite the fact that virtual reality is becoming increasingly important to manufacturing organisations' value [8]. Nevertheless, there has not been much research on the connection between virtual reality and the manufacturing industry.

This study represents an initial effort to arrange and add up the literature that science has to offer about the connection between virtual reality and the manufacturing industry. Several quantitative bibliometric investigations have been carried out to achieve this, utilizing software packages and computational methodologies that facilitate contribution in the process of knowledge generation. Gaps for further studies that consider the subject's active growth were identified, offer a thorough impression of the literature on virtual reality, and suggest some possible research avenues in this way.

Theoretically, by looking at how virtual reality in the manufacturing industry has developed, current tendencies, and recently discovered topics that are underrepresented and require further research, this study advances understanding of virtual reality in manufacturing. It could empower researchers to develop a thorough comprehension of a topic or benefit from the citation network's dissection into its component elements. This can help researchers by pointing out the most researched aspects of the topic, emerging patterns, and evolutionary orientations.

4.2. Research gaps

The terms "virtual reality," “augmented reality," “process simulation," “industrial internet of things," “industry 4.0 technologies," and “3D technologies" are still very popular and are being explored at the highest level compared to other concepts because the use of digital technologies in manufacturing is moving at a high pace.

During the most recent sub-periods (2018–2023), several terms were introduced. These consist of the “industrial internet of things,” “smart manufacturing systems,” and “digital twins.” These terms require further research because they are still new but important in the use of digital technology in manufacturing industries. Furthermore, cross-reality, smart technologies, and assembly assistance systems appeared in their first period, and in the next period, they disappeared. Due to their attachment to the research topic and their newness, they still need further research.

4.3. Limitations

The restrictions on bibliometric techniques, which are appropriate to our investigation, are not immune. At the outset, despite the benefits of the Scopus database, there is a possibility that pertinent papers exclusively accessible through other databases (such as ABI, Web of Sciences, and Inform/ProQuest) might have been overlooked, a common challenge in many bibliometric studies [57]. Also, documents like national periodicals, books, editorial content, and conference proceedings are omitted from this search strategy, even though they might be just as significant in the connection between virtual reality and the manufacturing industry [58]. Lastly, co-occurrence, co-citation, and citations were used, just like [59]. To complement our results, other bibliometric techniques, such as bibliographic coupling, may be applied. Therefore, the limitations indicated above offer endorsements for future research that aims to reinforce or improve upon them.

Conflicting interests

Regarding this research, the authors state that there are no possible conflicts of interest.

Funding

For this study article, the authors did not receive any funding.

References

1. Z. Guo et al., “Applications of virtual reality in maintenance during the industrial product lifecycle: A systematic review,” J. Manuf. Syst., vol. 56, no. July, pp. 525–538, 2020, doi: 10.1016/j.jmsy.2020.07.007.

2. S. Valin, A. Francu, H. Trefftz, and I. Marsic, “Sharing viewpoints in collaborative virtual environments,” Proc. Hawaii Int. Conf. Syst. Sci., p. 17, 2001, doi: 10.1109/HICSS.2001.926213.

3. B. Y. R. Packer, “MULTIMEDIA: FROM WAGNER TO VIRTUAL REALITY BY RANDALL PACKER (Includes excerpts from the forthcoming book ‘Multimedia: From Wagner to Virtual Reality,’” World, pp. 1–8, 2000.

4. S. Jayaram, H. I. Connacher, and K. W. Lyons, “Virtual assembly using virtual reality techniques,” CAD Comput. Aided Des., vol. 29, no. 8, pp. 575–584, 1997, doi: 10.1016/S0010-4485(96)00094-2.

5. F. C. Huang, K. Chen, and G. Wetzstein, “The light field stereoscope: Immersive computer graphics via factored near-eye light field displays with focus cues,” ACM Trans. Graph., vol. 34, no. 4, 2015, doi: 10.1145/2766922.

6. F. Chiarello, L. Trivelli, A. Bonaccorsi, and G. Fantoni, “Extracting and mapping industry 4.0 technologies using wikipedia,” Comput. Ind., vol. 100, no. February, pp. 244–257, 2018, doi: 10.1016/j.compind.2018.04.006.

7. M. L. Hoffmann Souza, C. A. da Costa, G. de Oliveira Ramos, and R. da Rosa Righi, “A survey on decision-making based on system reliability in the context of Industry 4.0,” J. Manuf. Syst., vol. 56, no. February, pp. 133–156, 2020, doi: 10.1016/j.jmsy.2020.05.016.

8. A. G. Frank, L. S. Dalenogare, and N. F. Ayala, “Industry 4.0 technologies: Implementation patterns in manufacturing companies,” Int. J. Prod. Econ., vol. 210, no. January, pp. 15–26, 2019, doi: 10.1016/j.ijpe.2019.01.004.

9. L. S. Dalenogare, G. B. Benitez, N. F. Ayala, and A. G. Frank, “The expected contribution of Industry 4.0 technologies for industrial performance,” Int. J. Prod. Econ., vol. 204, no. August, pp. 383–394, 2018, doi: 10.1016/j.ijpe.2018.08.019.

10. S. Choi, K. Jung, and S. Do Noh, “Virtual reality applications in manufacturing industries: Past research, present findings, and future directions,” Concurr. Eng. Res. Appl., vol. 23, no. 1, pp. 40–63, 2015, doi: 10.1177/1063293X14568814.

11. Y. Kang and S. Han, “An alternative method for smartphone input using AR markers,” J. Comput. Des. Eng., vol. 1, no. 3, pp. 153–160, 2014, doi: 10.7315/JCDE.2014.015.

12. T. Furuhata, I. Song, H. Zhang, Y. Rabin, and K. Shimada, “Interactive prostate shape reconstruction from 3D TRUS images,” J. Comput. Des. Eng., vol. 1, no. 4, pp. 272–288, 2014, doi: 10.7315/JCDE.2014.027.

13. J. Egger and T. Masood, “Augmented reality in support of intelligent manufacturing – A systematic literature review,” Comput. Ind. Eng., vol. 140, no. November, 2019, doi: 10.1016/j.cie.2019.106195.

14. E. Oztemel and S. Gursev, “Literature review of Industry 4.0 and related technologies,” J. Intell. Manuf., vol. 31, no. 1, pp. 127–182, 2018, doi: 10.1007/s10845-018-1433-8.

15. M. P. Pacaux-Lemoine, D. Trentesaux, G. Zambrano Rey, and P. Millot, “Designing intelligent manufacturing systems through Human-Machine Cooperation principles: A human-centered approach,” Comput. Ind. Eng., vol. 111, no. May, pp. 581–595, 2017, doi: 10.1016/j.cie.2017.05.014.

16. L. Li, “China’s manufacturing locus in 2025: With a comparison of ‘Made-in-China 2025’ and ‘Industry 4.0,’” Technol. Forecast. Soc. Change, vol. 135, no. May 2017, pp. 66–74, 2018, doi: 10.1016/j.techfore.2017.05.028.

17. A. A. Malik, T. Masood, and A. Bilberg, “Virtual reality in manufacturing: immersive and collaborative artificial-reality in design of human-robot workspace,” Int. J. Comput. Integr. Manuf., vol. 33, no. 1, pp. 22–37, 2020, doi: 10.1080/0951192X.2019.1690685.

18. Y. Zhang, G. Zhang, Y. Liu, and D. Hu, “Research on services encapsulation and virtualization access model of machine for cloud manufacturing,” J. Intell. Manuf., vol. 28, no. 5, pp. 1109–1123, 2017, doi: 10.1007/s10845-015-1064-2.

19. L. Da Xu, W. He, and S. Li, “Internet of things in industries: A survey,” IEEE Trans. Ind. Informatics, vol. 10, no. 4, pp. 2233–2243, 2014, doi: 10.1109/TII.2014.2300753.

20. J. Yan, Y. Meng, L. Lu, and L. Li, “Industrial Big Data in an Industry 4.0 Environment: Challenges, Schemes, and Applications for Predictive Maintenance,” IEEE Access, vol. 5, pp. 23484–23491, 2017, doi: 10.1109/ACCESS.2017.2765544.

21. A. Belhadi, K. Zkik, A. Cherrafi, S. M. Yusof, and S. El fezazi, “Understanding Big Data Analytics for Manufacturing Processes: Insights from Literature Review and Multiple Case Studies,” Comput. Ind. Eng., vol. 137, 2019, doi: 10.1016/j.cie.2019.106099.

22. X. Yao, J. Zhou, Y. Lin, Y. Li, H. Yu, and Y. Liu, “Smart manufacturing based on cyber-physical systems and beyond,” J. Intell. Manuf., vol. 30, no. 8, pp. 2805–2817, 2017, doi: 10.1007/s10845-017-1384-5.

23. S. Smith, G. C. Smith, R. Jiao, and C. H. Chu, “Mass customization in the product life cycle,” J. Intell. Manuf., vol. 24, no. 5, pp. 877–885, 2013, doi: 10.1007/s10845-012-0691-0.

24. A. Moeuf et al., “The industrial management of SMEs in the era of Industry 4 . 0 To cite this version : HAL Id : hal-01771941,” Int. J. Prod. Res., vol. 56, no. 3, pp. 1118–1136, 2018.

25. E. Marino, L. Barbieri, B. Colacino, A. K. Fleri, and F. Bruno, “An Augmented Reality inspection tool to support workers in Industry 4.0 environments,” Comput. Ind., vol. 127, p. 103412, 2021, doi: 10.1016/j.compind.2021.103412.

26. J. Cheng, W. Chen, F. Tao, and C. L. Lin, “Industrial IoT in 5G environment towards smart manufacturing,” J. Ind. Inf. Integr., vol. 10, pp. 10–19, 2018, doi: 10.1016/j.jii.2018.04.001.

27. T. Masood and J. Egger, “Augmented reality in support of Industry 4.0—Implementation challenges and success factors,” Robot. Comput. Integr. Manuf., vol. 58, pp. 181–195, 2019, doi: 10.1016/j.rcim.2019.02.003.

28. F. De Moya-Anegon et al., “Coverage analysis of Scopus: A journal metric approach,” Scientometrics, vol. 73, no. 1, pp. 53–78, 2007, doi: 10.1007/s11192-007-1681-4.

29. P. K. Priyan, W. I. Nyabakora, and G. Rwezimula, “A Bibliometric Review of the Knowledge Base on Capital Structure Decisions,” Vision, pp. 1–13, 2023, doi: 10.1177/09722629221140190.

30. J. Han, H. J. Kang, M. Kim, and G. H. Kwon, “Mapping the intellectual structure of research on surgery with mixed reality: Bibliometric network analysis (2000–2019),” J. Biomed. Inform., vol. 109, no. June, p. 103516, 2020, doi: 10.1016/j.jbi.2020.103516.

31. I. J. Akpan, “The Role of Virtual Reality Simulation in Manufacturing in Industry 4.0,” Systems, vol. 12, no. 26, pp. 1–22, 2024, doi: https://doi.org/10.3390/ systems12010026.

32. S. Choi, K. Jung, and S. Do Noh, “Virtual reality applications in manufacturing industries : Past research , present findings , and future directions,” Concurr. Eng. Res. Appl., no. March, pp. 1–24, 2015, doi: 10.1177/1063293X14568814.

33. K. Tomaszewska, “VR Technology in Manufacturing Processes – A Bibliometric Analysis,” Silesian Univ. Technol. Publ. House, no. 181, 2023, doi: http://dx.doi.org/10.29119/1641-3466.2023.181.37.

34. S. Kumar, R. Sureka, and S. Colombage, Capital structure of SMEs: a systematic literature review and bibliometric analysis, vol. 70, no. 4. Springer International Publishing, 2019. doi: 10.1007/s11301-019-00175-4.

35. W. I. Nyabakora, “Earnings management in public companies: a bibliometric review,” SN Bus. Econ., vol. 3, no. 9, 2023, doi: 10.1007/s43546-023-00546-w.

36. P. K. Priyan, W. I. Nyabakora, and G. Rwezimula, “A Bibliometric Review of the Knowledge Base on Financial Inclusion,” SN Bus. Econ., pp. 1–21, 2023, doi: 10.1177/09722629221140190.

37. N. J. Van Eck and L. Waltman, “Software survey: VOSviewer, a computer program for bibliometric mapping,” Scientometrics, vol. 84, no. 2, pp. 523–538, 2010, doi: 10.1007/s11192-009-0146-3.

38. W. I. Nyabakora, “Virtual Environments’ Knowledge Base: A Bibliometric Analysis,” Sci. Vis., vol. 15, no. 4, pp. 140–158, 2023, doi: 10.26583/sv.15.4.11.

39. D. Moher, A. Liberati, J. Tetzlaff, and D. G. Altman, “Academia and Clinic Annals of Internal Medicine Preferred Reporting Items for Systematic Reviews and Meta-Analyses :,” Ann. Intern. Med., vol. 151, no. 4, pp. 264–269, 2009.

40. P. Mongeon and A. Paul-Hus, “The journal coverage of Web of Science, and Scopus: A comparative analysis,” Scientometrics, vol. 126, no. 6, pp. 5113–5142, 2016, doi: 10.1007/s11192-021-03948-5.

41. P. Hallinger and J. Kovacevic, “A Bibliometric Review of Research on Educational Administration: Science Mapping the Literature, 1960 to 2018,” Rev. Educ. Res., vol. 89, no. 3, pp. 335–369, 2019, doi: 10.3102/0034654319830380.

42. E. Archambault, D. Campbell, Y. Gingras, and V. Lariviere, “Comparing bibliometric statistics obtained from the web of science and Scopus,” J. Am. Soc. Inf. Sci. Technol., vol. 60, no. 7, pp. 1320–1326, 2009, doi: 10.1002/asi.21062.

43. N. J. Van Eck and L. Waltman, “Citation-based clustering of publications using CitNetExplorer and VOSviewer,” Scientometrics, vol. 111, no. 2, pp. 1053–1070, 2017, doi: 10.1007/s11192-017-2300-7.

44. L. F. Agramunt, J. M. Berbel-Pineda, M. M. Capobianco-Uriarte, and M. P. Casado-Belmonte, “Review on the Relationship of Absorptive Capacity with Interorganizational Networks and the Internationalization Process,” Complexity, vol. 2020, 2020, doi: 10.1155/2020/7604579.

45. J. Wolfartsberger, “Analyzing the potential of Virtual Reality for engineering design review,” Autom. Constr., vol. 104, no. November 2018, pp. 27–37, 2019, doi: 10.1016/j.autcon.2019.03.018.

46. M. Lankes, J. Hagler, G. Kostov, and J. Diephuis, “Invisible walls: Co-presence in a co-located augmented virtuality installation,” CHI Play 2017 - Proc. Annu. Symp. Comput. Interact. Play, pp. 553–560, 2017, doi: 10.1145/3116595.3116609.

47. L. Vagner, K. Valaskova, P. Durana, and G. Lazaroiu, “Earnings management: A bibliometric analysis,” Econ. Sociol., vol. 14, no. 1, pp. 249–262, 2021, doi: 10.14254/2071-789X.2021/14-1/16.

48. X. T. R. Kong, H. Luo, G. Q. Huang, and X. Yang, “Industrial wearable system: the human-centric empowering technology in Industry 4.0,” J. Intell. Manuf., vol. 30, no. 8, pp. 2853–2869, 2018, doi: 10.1007/s10845-018-1416-9.

49. H. S. Kang et al., “Smart manufacturing: Past research, present findings, and future directions,” Int. J. Precis. Eng. Manuf. - Green Technol., vol. 3, no. 1, pp. 111–128, 2016, doi: 10.1007/s40684-016-0015-5.

50. R. Y. Zhong, X. Xu, E. Klotz, and S. T. Newman, “Intelligent Manufacturing in the Context of Industry 4.0: A Review,” Engineering, vol. 3, no. 5, pp. 616–630, 2017, doi: 10.1016/J.ENG.2017.05.015.

51. S. K. Ong, M. L. Yuan, and A. Y. C. Nee, “Augmented reality applications in manufacturing: A survey,” Int. J. Prod. Res., vol. 46, no. 10, pp. 2707–2742, 2008, doi: 10.1080/00207540601064773.

52. J. M. Davila Delgado, L. Oyedele, P. Demian, and T. Beach, “A research agenda for augmented and virtual reality in architecture, engineering and construction,” Adv. Eng. Informatics, vol. 45, no. May, p. 101122, 2020, doi: 10.1016/j.aei.2020.101122.

53. F. Zorriassatine, C. Wykes, R. Parkin, and N. Gindy, “A survey of virtual prototyping techniques for mechanical product development,” Proc. Inst. Mech. Eng. Part B J. Eng. Manuf., vol. 217, no. 4, pp. 513–530, 2003, doi: 10.1243/095440503321628189.

54. A. Syberfeldt, O. Danielsson, and P. Gustavsson, “Augmented Reality Smart Glasses in the Smart Factory: Product Evaluation Guidelines and Review of Available Products,” IEEE Access, vol. 5, pp. 9118–9130, 2017, doi: 10.1109/ACCESS.2017.2703952.

55. X. F. Liu, M. R. Shahriar, S. M. N. Al Sunny, M. C. Leu, and L. Hu, “Cyber-physical manufacturing cloud: Architecture, virtualization, communication, and testbed,” J. Manuf. Syst., vol. 43, no. November 2018, pp. 352–364, 2017, doi: 10.1016/j.jmsy.2017.04.004.

56. S. P. Nerur, A. A. Rasheed, and V. Natarajan, “The intellectual structure of the strategic management field: An author co-citation analysis,” Strateg. Manag. J., vol. 29, no. 3, pp. 319–336, 2008, doi: 10.1002/smj.659.

57. P. Jacso, “The pros and cons of computing the h-index using Web of Science,” Online Inf. Rev., vol. 32, no. 5, pp. 673–688, 2008, doi: 10.1108/14684520810914043.

58. M. del P. Casado-Belmonte, M. de las M. Capobianco-Uriarte, R. Martinez-Alonso, and M. J. Martinez-Romero, Delineating the Path of Family Firm Innovation: Mapping the Scientific Structure, vol. 15, no. 8. Springer Berlin Heidelberg, 2021. doi: 10.1007/s11846-021-00442-3.

59. V. Tiberius, H. Schwarzer, and S. Roig-Dobon, “Radical innovations: Between established knowledge and future research opportunities,” J. Innov. Knowl., vol. 6, no. 3, pp. 145–153, 2020, doi: 10.1016/j.jik.2020.09.001.