Extended Reality Applications for CNC Machine Training: A Systematic Review

Abstract

Extended reality (XR) as an immersive technology has gained significant interest in the industry for training and maintenance tasks. It offers an interactive, three-dimensional environment that can boost users’ efficiency and safety in various sectors. The present systematic review provides information based on a Scopus database search for research articles from 2011 to 2024 to expose 19 selected studies related to XR developments and approaches. The purpose is to grasp the state of the art, focusing on user training in goals or tasks that involve computer numerical control (CNC) machines. The study revealed approaches that broadly employed XR devices to execute diverse operations for virtual CNC machines, offering enhanced safety and skills acquisition, lessening the use of physical machines that impact energy consumption or the time invested by an expert worker to teach an operation task. The articles highlight the advantages of XR training versus traditional training in CNC machines, revealing an opportunity to enhance learning aligned to the industry 4.0 (I4.0) paradigm. Virtual reality (VR) and augmented reality (AR) applications are the most used and are mainly centered on a single-user environment. In addition, a VR approach is built as a proof of concept for learning CNC machine operations, considering the key features identified.

1. Introduction

Industry 4.0 (I4.0) drives using disruptive technology, where innovation completely revolutionizes the work process [1]. I4.0 refers to implementing technologies related to pervasive digitalization in smart manufacturing processes such as virtual reality (VR) and the Internet of Things [2]. For instance, VR replicates a company’s facility to support decision making in manufacturing systems and provides a deep understanding of a simulation [3]. This digital transformation is challenging for industry and academic sectors because traditional and passive learning methods are becoming obsolete [4].

The adoption of I4.0 core technologies such as VR, augmented reality (AR), and mixed reality (MR) for training tasks have unique characteristics and promise to open new dimensions in the user experience [5]. These technology solutions modify how the user interacts and perceives information from the environment [6], offering training customization [7] or enhancing worker efficiency [8]. Regarding immersion, VR has reached its full potential to immerse users in exploring virtual worlds through head-mounted displays (HMD) and great advancements in haptic devices [9].

In contrast, AR developments enable users to see the local environment by overlaying digital objects using mobile devices or glasses [10]. MR applications are within the spectrum of AR and VR developments, enabling virtual objects to be aware of the real ones through sensors, interacting with the physical environment, and vice versa [11]. Furthermore, gloves or haptic feedback (HF) devices are employed in training activities or task instructions in extended reality (XR) applications to enhance the user experience and mimic the sensation of a blow or wound on the physical body [12]. In essence, XR is a term that encompasses VR, AR, and MR to describe the user experience in different reality modes [13] and grades of immersion.

Computer numerical control (CNC) machines are valuable equipment that might be integrated into the I4.0 production model [14]. These machines with optimal parameters produce precise pieces and enhance machining efficiency [15], executing detailed and specific code to provide accurate and repeatable results for manufacturing parts [16]. Thus, process optimization becomes relevant for cutting operations as tool life usage and dynamic stability improve machined part quality [17,18]. CNC machines are sophisticated and complex, with high costs and accelerated obsolescence of hardware and software, requiring operators to keep up to date with solid technical knowledge and skills [19,20].

Trained personnel can optimize the machine’s performance for higher productivity and less material waste [21]. In CNC lathe training, AR job sheets provide practical experiences while saving raw materials, cutting tool costs [22], and assist users in optimizing their abilities [23]. Moreover, training tasks in CNC machines take time and practice to acquire competent skills, which can be expensive. The typical tasks performed in CNC machines relate to tool load operation, making the toolset compensation, programming the code, and installing the cutting tool [24]. The execution of such tasks in a virtual environment (VE) integrates instructions and guidelines that provide clarity and speed of task completion, leading to a more intuitive and enriched training experience [25], allowing the operators to reinforce their psychomotor, spatial, and cognitive skills through open-ended practice [26]. The above tasks and skills are addressed in the present review.

The manufacturing industry needs skilled workers to perform specialized tasks [27]. Lack of proper training may lead to production inefficiencies [28] and cause risks to staff and equipment [29]. Rotatory and temporal personnel hamper the usage of new technologies because the employees do not have the same skill level [30]. Hence, industry and academic sectors are embracing modern technologies and blended learning approaches, such as disruptive teaching, driven by technology to boost user interest and meet their needs, prioritizing strategies [31,32]. For example, blending VR simulation with machines fosters students’ understanding by enabling them to repeat the procedures at their own pace to ensure the teaching effect, reducing material waste and avoiding machine tool accidents [33,34]. Also, machine manufacturers and vendors are implementing XR training as a complementary service to add value and customer loyalty [35,36].

Alternatively, universities foster cooperation with industry and government to raise excellence with the latest emerging trends toward I4.0, such as artificial intelligence (AI) and VR [37]. Furthermore, it is necessary to develop frameworks integrating knowledge, attitudes, and skills that empower students and help them succeed in the workforce, narrowing the gap due to outdated curricula and limited use of resources for laboratory practices, as well as meeting the requirements between university graduates and industry needs [38,39]. Training future students and industrial trainees requires a paradigm shift to exploit XR learning environments to close the breach between theory and practice [40].

Skilled operators are becoming scarce (e.g., declining population and retirement of workers) and require time and space to grow skills [41]. In this regard, there are endeavors oriented to use AI models to (1) replicate operators’ experiences of safety and quality control to enhance the grade of CNC automation and train new operators, reducing the workload on experienced operators [42]; and (2) improve knowledge transfer in critical industrial settings and extract operators’ implicit procedural knowledge, as well as assess their decision-making processes [43].

XR applications have gained considerable attention in the industry to speed up training in the workforce, avoid halting the production line while training new operators, and provide spaces to build demanded experience, practice, and skills [41,44]. Physical operations and tasks users perform on CNC machines, replicated in XR environments within academic and industrial domains, are an open issue for adopting technology-enhanced learning paradigms. Therefore, the present systematic review addresses the gap by focusing on XR studies for teaching and training users in CNC machine tasks commonly executed in industrial environments or academic laboratories.

The motivation to pursue the present review is to grasp the state of the art derived from XR developments and approaches, focusing on user training in operations that involve CNC machines. We performed a systematic literature review of XR applications employed for CNC machine training. Our review contributes to identifying the hardware and software used to build XR applications, tasks performed in the virtual environment (VE), haptic feedback support, experimental settings scenario, and types of CNC machines employed, revealing a mature panorama for learning with novelty toward I4.0. In addition, a VR application as a proof of concept is designed for teaching and training operators in CNC machine tasks, considering key features identified in the studies reported.

2. Background and Relevant Research

CNC machines are electromechanical products that include CNC lathes, CNC milling machines, CNC grinding machines, machining centers, and CNC drilling machines [45]. Moreover, CNC wire electrical discharge machining (WEDM) and laser cutting machines are crucial in the aerospace industry to machine nitinol workpieces, where previous machines cannot be employed [46]. In addition, CNC carving is a widely used method of industrial subtractive manufacturing of wood, plastics, and metal products [47]. The parts of a typical CNC machine comprise sensors and actuators, controllers, a machine tool, a driving system, and a feedback system [48]. The control unit processes all the code instructions and signals received from different subsystems, controlling the CNC machine’s actions by sending the correct outputs and executing the machining of a piece.

Figure 1 depicts the CNC machine parts, such as an enclosure, a base to be fixed in a workplace to support the systems, and a door access to place the workpiece to be machined. The spindle secures a tool holder to install an end mill or other tool in compliance with the service order, where different axes work according to the code instructions for machining a raw material. CNC machine tools are relevant in manufacturing but impact the environment by their high total energy consumption and low energy efficiency [49]. Thus, it is vital to implement strategies such as XR technologies in training courses that minimize the use of physical CNC machines. They allow experts and operators to review and execute teaching and training procedures as often as necessary for trainees’ better understanding of how to operate the CNC machine efficiently once they are ready.

Several endeavors have addressed literature reviews, systematic reviews, and surveys related to XR technologies with teaching and training approaches for CNC machine tasks (e.g., operation, assembly, or maintenance). In this context, XR offers several advantages over hands-on training with industrial machines. For instance, Devagiri et al. [50] analyzed the trends of AR and AI applications widely used in testing, assembly, maintenance, and product design, highlighting the challenges in safety and policies. Similarly, Baroroh et al. [51] exposed that most of the literature they reviewed was related to assembly tasks and positioning functions to reduce mental workload (e.g., CNC tools tracking). Likewise, Daling and Schlittmeier [52] explored XR assembly tasks, finding that AR leads to better results associated with task completion time and accuracy, and VR increases assembly performance in the long term. Kaplan et al. [53] presented a meta-analysis of XR training effects, concluding that immersive technology’s value lies in delivering training where traditional methods are costly and risky. Additionally, Akpan conducted a bibliometric analysis regarding VR simulation in manufacturing in the context of I4.0, highlighting it as a crucial technology in the predictive digital twin (DT) [54].

Hence, XR technology supports the creation of ad hoc safe and risk-free environments, which can be as effective as conventional training [55]. Egger and Masood [56] presented a literature review related to AR research, exposing certain shop floor operations such as maintenance tasks for a CNC lathe. Soori and Arezoo [57] reviewed articles on CNC milling and turning machine tools, focusing on the methods for modeling operations in VE (e.g., mathematical concepts that model virtual CNC machines’ cutting forces). Yin et al. [58] discussed state-of-the-art studies with an AR-assisted DT perspective, classifying them in engineering lifecycle stages. They found that CNC machine research addressed the design and production stages. Also, Asad et al. [59] examined human-centric DT articles, identifying an extensive use in training robotic systems in VE. Chan et al. [60] explored XR technologies in welding training to help students develop psychomotor skills, reporting problems in traditional training such as economic factors and technical aspects (e.g., high costs and accident proneness).

Concerning academia, Hensen [61] reviewed XR research to support learning and teaching education. They found that most XR deployments mainly focused on self-regulated learning applications, followed by knowledge presentation implementations. Van der Want and Visscher [62] presented a literature review on VR characteristics used in preservice teacher education, reporting positive effects on student teachers’ motivation, self-efficacy, and pedagogical skills. Brancaccio et al. [63] examined the readiness of virtual laboratory applications in engineering departments. They identified steps to introduce VR smoothly in the classroom and the need to develop a framework for designing and evaluating blended learning from a research perspective. Rojas-Sánchez et al. [64] investigated the use of VR applications in education, remarking that at the beginning of COVID-19, students pushed for VR applications in the learning process despite institutions being reluctant to support them. Further, Lai and Cheong [65] explored XR for teaching and learning engineering mathematics, proposing a framework with four stages: design, prototype, validate, and iterate. They reported technical reflections for educators, fostering the integration of XR-enhanced learning.

3. Materials and Methods

We conducted a systematic literature review guided by PRISMA [66]. The scope of the research focused on journal articles about user teaching and training on tasks related to CNC machines using XR. The method used was a search in the Scopus Elsevier^® database on 29 February 2024 and was limited to document type, language (written in English), within the subject areas of computation and engineering, and published from 2011 up to date, considering that the term I4.0 was introduced at the Hannover Fair in that year as a term that comprises enabling technologies such as XR [67].

In terms of the aforementioned respects, Table 1 shows the search string used. The results delivered 55 records, excluding two corresponding to a conference proceedings book and a book chapter. Table 2 depicts the inclusion and exclusion criteria for this systematic literature review. The articles focused on a teaching or training task using an application of XR and reported the use of a CNC machine virtual model. Two authors independently screened 53 records by title, abstract, and keywords; both needed to agree to include or exclude a record. In case of a tie, a third author submitted a vote to make a final decision. As a result, 23 records were rejected, wherein 17 presented an XR application but not oriented to a CNC machine task, 3 were related to materials science, 2 were linked to the health sector, and 1 was an exploratory study. The 30 articles filtered from the screening process were obtained, except 2 (1 not retrieved and 1 retracted by the journal).

The remaining 28 articles were reviewed by three authors minimum. In case of disagreement, a discussion was held until a consensus was reached. In this stage, nine studies unrelated to CNC machine tasks or XR application were excluded. Subsequently, the authors reviewed 19 studies, manually extracting the attributes mentioned in Section 4 and classifying them using an Excel spreadsheet; Figure 2 illustrates a flow diagram of the articles’ selection process. It details the identification, screening, and inclusion stages. Table 3 presents the distribution of articles by journal. The first column lists the journal’s name, the following column the number of articles published, and the last column the year of publication. It is worth noting that the years with the highest number of articles published were 2017, 2019, and 2020, each with three articles.

The present systematic review was oriented to answer the three research questions below. As a result, a classification with relevant features of XR applications serves as a guide to designing a proof of concept and developing a training program for CNC machine operators based on a work procedure.

• (1). What type of XR applications are used for CNC machine training?

• (2). What hardware and software are mainly employed in XR applications for CNC machine training?

• (3). What tasks are addressed in XR applications for CNC machine training?

4. Results

This section presents a brief description of the studies and a discussion. The authors analyzed 19 selected articles and classified them according to the following attributes: type of XR application exposed, the CNC machine type, the goal or task, the hardware and software employed, the use of HF devices, experiment settings, and comments to pinpoint the main characteristics. In this regard, Table 4 presents a stratification of XR applications. Eleven developments are VR, five are AR, and four are MR. Concerning VR applications, Krajčovič et al. [68] implemented a drilling workplace in the teaching process to operate a machine, reporting that the students proposed more optimizations for the layout workspace, ergonomics, and material flow than pupils in conventional training.

Hirt et al. [69] compared the users’ learning efficiency using VR applications versus traditional teaching, addressing four tasks in both scenarios and evaluating them on a physical machine. The performance is similar in both groups, with subtle advantages for the VR group. Kroupa et al. [70] created a virtual assembly and disassembly of a machine with its elemental construction pieces (e.g., frame, table, crossbar, automatic tool changer) where users can learn the construction process of industrial equipment.

Furthermore, Ottogali et al. [71] proposed a framework to model a machine tool, describing an action to grab a cutter. Rogers et al. [72] reported a VR development where users follow instructions to program a CNC machine with G-code. Wasfy et al. [73] presented a framework for online CNC training milling and turning machines capable of executing code on virtual machines (e.g., change travel and rotation speed). Moreover, Chen et al. [74] designed a VR training environment to compare sequence-based versus context-based teaching by manipulating the controller to simulate operations and behaviors. Similarly, Kao et al. [75] emulated a CNC’s functions of a controller for tool path simulation and program interpretation and processing. Finally, Macías et al. [76] created a cyber-physical laboratory for engineering training in the assembly process of a drilling machine (e.g., fixed base, frame, turret) and machining processes.

Regarding AR systems, Monroy et al. [77] developed an application to train users in machine operation by overlaying text instructions, animations, and videos on a physical CNC machine. Similarly, Tatić and Tešić [78] presented an approach to guide the operators on safety and maintenance procedures. They described a use case to operate a lathe where the application shows information according to the worker’s experience. Samala and Amanda [79] created an application for CNC milling machine simulation (e.g., rotate a 3D model and move the x, y, and z axes). Lai et al. [80] implemented an instruction system on a deep learning-based tool detection to support the assembly of a CNC carving machine. Concerning MR applications, Chuang et al. [81] built a prototype with HF that exerts the cutting force as in a traditional manual milling operation, where the operator relates milling parameters and conditions. Tzimas et al. [82] helped operators with instructions to fix a vise on a turning and machining center table and clamp parts onto it.

In addition, studies related to content generation and tutoring systems were found. For instance, Carretero et al. [83] presented a methodology to create training courses to learn how to set up and operate a virtual milling machine. They reported that trainees and tutors perceived an increment in trainees’ confidence in using physical machines. Longo et al. [84] developed a framework to integrate AR content and tutoring systems to improve operators’ skills. The workers used an armband, and the application interpreted gestures to evaluate the users on setup operations for cushion slide manufacturing.

Mourtzis et al. [85] proposed a collaborative framework to support remote machine maintenance, comparing it with traditional maintenance in time and costs. Wang and Qi [86] deployed a system for multi-user services and applications for three laser-cutting machines, including cutting head design, layout editing, and cutting machine inspection. Two articles reported as AR developments [82,86] were classified as MR since they employed sensors to modify the virtual objects’ behavior. Despite the above functionality in MR applications supporting the interaction of physical and virtual objects, in a review for innovative learning, the amount of VR applications outnumbered AR and MR studies [87].

Concerning CNC machine type, the most predominant reported models are lathe and milling machines. In addition, two studies reported drilling machines, whereas grinding, WEDM, carving, and laser cut models are each in single articles. Regarding the goal or task performed, most articles focused on the operation, maintenance, and assembly of machines, citing the safety benefits for pupils and workers. Two studies presented collaborative environments stressing the importance of teamwork, whereas the rest were single-user XR applications. It is worth noting the interest in framework developments for collaborative VE to promote knowledge exchange toward the industrial metaverse where areas of knowledge merge [88], bridging the gap in VE, where operators or trainees interact through XR applications and collaborate with experts from various domains [89].

As for hardware, seven VR applications took advantage of the HMDs, and the rest were PC-based, as presented by [74,75], which implemented a comprehensive set of CNC machine functions. Regarding AR developments, two applications used glasses, and the remaining studies employed computer or mobile devices. As for MR applications, two studies utilized HoloLens, and the rest were mobile devices plus sensors. Concerning software platforms for developing XR applications, 12 articles preferred the Unity game engine, and 2 utilized EON Studio. In addition, studies reported toolkits widely known such as Vuforia and MRTK and application programming interfaces like WebXR to enhance or enable immersive VE functionality. Furthermore, concerning the use of HF devices, three studies explicitly reported HMDs with haptic controllers [69,70,71], but one of them pointed out some limitations for complex task simulation [69].

In this vein, [84] employed an armband, and [81] developed an HF prototype, highlighting the importance of using such devices and reporting their benefits. In contrast, [74] mentioned the difficulty of operating handwheel steering in CNC machine operations using a computer mouse, resulting in the incorrect movement direction. On the other hand, the experiment setting most exhibited is an academic scenario involving 3D models of CNC machines for validation and testing purposes. Furthermore, [69,84], regardless of the traditional or XR training group, evaluated users’ learning in a physical machine, obtaining slight advantages for the XR group. In addition, six studies ran tests in industrial scenarios. A couple of them integrated collaborative features for content generation, while others focused on machine operations and maintenance.

5. Design of Proof of Concept

This section presents an in-progress development of a proof of concept considering key aspects and attributes identified in the reviewed research. The proposal consists of a VR application for teaching and training operators on a CNC machine task related to mounting a tool, placing the workpiece to be machined, and starting the machine. For this purpose, the worker receives face-to-face teaching instructions and performs part of a work procedure on the VR application. In the VR environment, the operator observes a list of technical step-by-step instructions for the procedure and visual clues pointing to the tool names to execute the operational tasks.

The type of CNC machine modeled is a five-axis Hermle C-400 model milling and turning center, described in Figure 1. The equipment is located at Centro de Tecnología Avanzada (CIATEQ) facilities and is used to machine several workpieces for local industries, such as automotive and food. In terms of hardware, the development employs a desktop computer with an i7 processor and an RTX 2080 GPU with 8 GB. The user experiences the VE through an HTC Vive with haptic controllers, earphones, and tracking stations for a complete immersion. The 3DEXPERIENCE platform is employed to develop the VE scenario [90]. The 3D model incorporates restrictions, specifies predefined positions, and configures equipment attributes such as machine home and travel limit.

Table 5 shows the travel limits of the moving parts of the CNC machine according to the physical machine specifications and the development tool. For instance, the command Door, whose values range in millimeters from −550 to 0, is assigned to the right and left doors to enable their movement. Likewise, the limits setup applies for animation of each axis. In this context, Figure 3 depicts the VE mockup, where the machining center is in the middle of an industrial scenario, along with a toolbox and a table with a vise and accessories. The operator experiences high-quality illumination and sound in the VE surroundings and can freely move and interact with several objects (e.g., panel and tools), as well as hear sounds when an event happens due to user proximity to a particular object or when grabbing an item to perform a task.

In addition, Figure 4 depicts a series of steps, part of the procedure to mount the tool on the CNC machine and machine a raw material. Figure 4a displays the toolbox and a table with the necessary prefabs for machining and fixing a workpiece on the machine, such as nuts, threaded studs, clamps, cutting tools, tool holders, vise, and some measuring instruments. The operator’s task to be executed consists of identifying and checking all needed tools and accessories for a machining operation. In this sense, the worker can grab an object, change its location, rotate it, and visualize 3D model details. Figure 4b illustrates the task of mounting the tool holder in the machine’s spindle and later placing an end mill tool (e.g., flat, bull, ball, or roughing).

In Figure 4c, the worker aligns and bolts the vise to the machine table to place a new block of material to be machined, which involves gently tapping with a rubber mallet to mount and fit it perfectly in the vise. Once the cutting tools and the piece to be machined are placed and fixed, the next step for the machine operator is to define the machining origin and make the height compensation of the cutting tools to proceed with the execution of the machining program. As shown in Figure 4d, the virtual CNC simulation starts, and the axes work together to machine the raw material. The X, Y, and Z axes place the tool over the raw material to be machined, whereas the A-axis rotates slightly, and the C-axis rotates left 90°. The proof of concept facilitates the visualization of the potential of immersive XR applications for teaching and training operators and evaluating the execution of some procedural tasks when operating a CNC machine (e.g., placing the right tool holder and the end mill according to the machining objective).

6. Discussion

The complexities of CNC technology, with a continuous increase in the information required for operation and maintenance, cause an increment in the user’s cognitive effort and proneness to commit errors or faults. Hence, guidance or support is beneficial for operators to restore CNC machines to safe conditions [91]. Even though infrastructure monitoring and rapid quality diagnosis of CNC processes are becoming relevant, there is still the need to incorporate intelligence in CNC tools to ensure reliability and productivity, for instance, in real-time cutting operations [92] in which AI and machine learning (ML) are applied to the cutting forces and cutting tool wear prediction by optimizing machining parameters [93]. On the other hand, operators of CNC machines need different, specialized, and broad skills, while expert operators are scarce. Henceforth, training novel users implies high costs [91]. In I4.0, cognitive DT combines human tacit knowledge with DT models with the ability to monitor the status of the corresponding manufacturing elements, deal with unforeseen situations, and hence support decisions [94]. In this sense, capturing the expert knowledge of CNC operators is essential for expertise-sharing paradigms to train new operators by achieving digital identities not only with technical knowledge and skills but also with ways of thinking, ways of acting, or reasoning [95], and transferring them to the VE or metaverse.

The meta-operator emerges from the fusion of metaverse and industry 5.0 to address the worker’s knowledge and skills, using the latest XR technologies [96]. The operator requires the support of haptic devices to replicate the realism of virtual elements and direct hand–object manipulation through tactile and kinesthetic feedback. Therefore, the use and research of new materials (e.g., nanocomposites), multimodal sensory technologies [97], the combination of driving mechanisms (e.g., electro-adhesion with ultrasonic signals), and the integration of different haptic cues are essential [98]. The goal is to transmit sensations to the mechanoreceptors of human skin [99], enabling the sense of touch to deliver tactile experiences and skills transfer [100]. Concerning maintenance tasks, the prognosis is verified by an AI avatar to aid in repair planning and training for maintenance technicians. Moreover, a haptic-based robot teleoperation system for blade repair grinding has been developed [101].

6.1. Implications

The present research attempts to provide an overview of the state of the art concerning the XR developments and approaches focused on user training in tasks and operations of CNC machines in academic and industrial domains. The insights presented reflect a snapshot of the current state of the literature, pointing out that the use of XR technologies for training improves spatial and psycho-motor skills [26,102], enables real-time decision-making capability [103], and aids in dealing with the operator’s cognitive load due to the complexities of CNC technology [43,91]. In addition, regarding hardware, HMD equipment, mobile devices, and glasses are the most employed in training, and there is a growing research interest in haptic feedback capabilities to enrich visual and auditory user channels that enhance the immersion experience in VEs.

6.2. Limitations

The main limitation of this study is that the authors identified few eligible studies, mainly due to the decision to restrict the search only to a well-known database such as Scopus, as well as the search filters applied (e.g., document type, date range, and language), which could have left out other relevant studies. Further studies should consider conducting in-depth research, opening the date period, and covering other studies from scientific venues such as congresses and conferences, supported by a bibliometric analysis to discover a link among the XR applications, researchers, and human interaction technologies. In light of the review findings, developers, researchers, and policy-makers can consider the actions to take concerning the design and the use of XR technologies. In this vein, this enables the creation of training programs that incorporate multimodal human–computer interaction to enrich user experiences in industrial and academic contexts.

6.3. Future Research

This systematic review depicted XR applications for CNC machine training, showing a panorama to explore advancements in academic and industrial sectors. The design of a proof of concept helps contrast theoretical versus practical issues and the advantages and drawbacks of XR technology. A couple of research directions can be pursued initially to develop an ontology that integrates expert knowledge related to the settings and operation of CNC machines and to create an AI avatar to interact with the trainee in the VE. Second, the design and construction of low-cost haptic devices with several ad hoc shapes to the objects in virtual scenes enable more immersive and interactive VEs, for instance, working tools according to their physical shape, displaying their main features, and supporting the work on the assembly of a CNC machine.

7. Conclusions

XR technology is pivotal in transforming CNC machine training methodologies, providing immersive and risk-free environments for skills development. The literature review illustrated diverse dimensions of XR applications for CNC machine training, including VR, AR, and MR. The study revealed a landscape ripe with innovation for industrial training. In this sense, one study indicated that HF devices improved user experience and training outcomes, emphasizing the importance of sensory feedback in XR environments [81]. Regarding the hardware and software attributes, HMD equipment, mobile devices, and glasses were the most commonly reported, and the Unity engine was used for XR applications development and Vuforia to extend functionalities. In addition, MR applications stood out for their ability to modify virtual object behavior and enhance realism to immerse the user in the highest learning experience.

On the other hand, most XR applications were deployed in academic settings in classrooms and a few in engineering laboratories, being tested primarily on students and educational personnel such as teachers and laboratory technicians. Developing collaborative XR environments fosters teamwork and knowledge exchange, promising further advancements in skills development in the industrial metaverse. Overall, XR presents a paradigm shift in academic and industrial education, offering enhanced safety and skills acquisition, lessening the use of physical machines that impact energy consumption or the time invested by an expert worker in a training task, all of the above without the need to stop or interrupt a production process. Finally, the proof of concept presented a VR development that offers a tool to train users on typical tasks of the operation of a CNC machine. This approach facilitates envisioning the potential of these kinds of applications.

Author Contributions

Conceptualization, J.M.I.K. and Ó.H.-U.; methodology, all authors; software, J.M.I.K., R.A.L.-M. and Ó.H.-U.; validation, all authors; investigation, all authors; writing—original draft preparation, J.M.I.K. and Ó.H.-U.; writing—review and editing, L.A.C.-R. and Ó.H.-U. All authors have read and agreed to the published version of the manuscript.

Acknowledgments

This study was partially supported by the grant CONAHCYT-CIATEQ CVU 1001895 and CONAHCYT National Research System of Mexico. We express our gratitude to Alberto Hernández Padilla for the CNC machine operation support.

Conflicts of Interest

José Manuel Ibarra Kwick has received the facilities from Diseño y Manufactura Digital, a VIAS3D Company, to use a software license 3DEXPERIENCE to develop the proof of concept design. The authors declare no conflicts of interest.

Footnotes

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Figures and Tables

Figure 1. CNC machine, Hermle model C400.

Figure 2. Flow diagram showing the articles selection process adapted from PRISMA [28].

Figure 3. Virtual shop floor mockup.

Figure 4. Procedure for machining a workpiece: (a) Toolbox, table, and accessories; (b) tool holder and end mill mounting; (c) placement of the vise and adjusting the raw material with a rubber mallet; (d) execution of the virtual CNC machining simulation.

Table 1

Systematic literature review search string.

Database	Search String
SCOPUS	TITLE-ABS-KEY((train* OR teach*) AND (xr OR “extended reality” OR vr OR “virtual reality” OR ar OR “augmented reality” OR mr OR “mixed reality”) AND (factory OR industr* OR manufactur*) AND (“computer numerical control” OR ((cnc OR machine) AND (milling OR turning OR drilling OR grinding OR lathe OR tool))))

Table 2

Inclusion and exclusion criteria.

Inclusion Criteria	Exclusion Criteria
The article focuses on a CNC machine teaching or training task using an application of XR and a CNC model.	The article reports XR applications on training without referring to a CNC machine task.
The article discusses an XR application in computation and engineering fields.	The article contains XR applications for other sciences (e.g., health).
The article declares hardware, software, and experimental settings (at least two).	Reviews, surveys, or exploratory studies are not considered.

Table 3

Journals’ distributions by number of published articles and year.

Journal	# Articles	Year
Appl. Sci.	2	2019 and 2020
Comput. Appl. Eng. Educ.	2	2016 and 2019
Comput. Educ. J.	2	2013 and 2017
Int. J. Interact. Des. Manuf.	2	2019 and 2020
Comput. Ind.	1	2017
Comput. Ind. Eng.	1	2017
Int. J. Adv. Manuf. Technol.	1	2011
Int. J. Interact. Mobile Technol.	1	2023
Int. J. Mechatron. Manuf. Syst.	1	2021
J. Manuf. Syst.	1	2020
MM Sci. J.	1	2018
Multimedia Tools Appl.	1	2021
Sensors	1	2022
Sustainability	1	2022
Virtual Reality	1	2023

Table 4

The XR-CNC machine research studies.

Ref.	XR	CNC	Goal/Task	Hardware	Software	HF	ExperimentalSettings	Comments
[68]	vr	drill	layout optimization,operation	3dscan, hmd	un	n	as, 30 participants.	Students used VR technology, giving more suggestions of layout and ergonomics than traditional learning.
[69]	vr	gr	operation	htc	-	y	as, 19 users mostly students (10 using VR and 9 a CNC).	VR group using the CNC machine made fewer errors after concluding the training tasks (e.g., exchange a grinder wheel).
[70]	vr	mill	assembly	oc	un	y	as, machine disassembling.	Application for a better understanding of machine tool construction.
[71]	vr	lth,mill	operation	htc, moc	fr,un	y	as, performing a manual task with a machine panel.	The framework models I4.0 processes using JSON files. They developed three industrial scenarios for testing purposes.
[72]	vr	lth,mill	operation	ocdk	un	n	as, 43 students (21 using VR and 22 a CNC).	The student’s head is used as a pointer to interact with the VE but generates an issue when importing code to the lathe.
[73]	vr	lth,mill	operation	pc	flash,fr	n	as, testedengineering course.	No differences in outcomes for VE and traditional learning, VE courses could substitute classroom teaching.
[74]	vr	mill	operation	pc	ct, eon,ps, vb	n	as, 40 students (21 sequence and 19 context-based).	Students with context-based training got better results for skill learning, operation time, and error avoidance.
[75]	vr	wedm	operation	pc	eon,fr, vb	n	as, a user performed a cutting operation.	The user practices in a VR application before using a machine, reducing the risks and costs of traditional learning.
[76]	vr	drill	assembly,process automation	pcplc	lv,vrml	n	as, 200 students.	The VR application has been implemented on educational campuses, enhancing students’ learning process.
[77]	ar	lth,mill	operation	ar-hmd,gl	bl,un, vf	n	as, 16 students, teachers and technicians.	The AR application guides novice pupils to work with a CNC machine and reduces the risks of laboratory practices.
[78]	ar	lth	maintenance,operation,	mo	un,vf	n	is, tested by an expert and a novice worker.	The worker performs a checklist to operate a CNC machine according to his experience level.
[79]	ar	mill	simulation 3d axis	mo	bl, figma, un, vf	n	as, tested by internal developers.	The AR application provides text, audio, images, and 3D object animations to understand machine’s working principles.
[80]	ar	carv	assembly	cam,pc	un	n	as, 20 participants (10 used AR, 10 paper manual).	Multi-modal AR instructions reduce assembly time and errors, using deep learning for tool detection.
[81]	mr	mill	operation	hol,jn,ss	mrtk, ros,un, vf	y	is, 31 operators (1 year of milling work experience).	The users reduced time to adjust the workpiece location using an HF device.
[82]	mr	lth, mill	fixture and clamping parts	ad, cam,mo,ss	3ds, sw,un,vf	n	as, pilot test.	Sensors improve the accuracy of virtual positions vs. world objects. A database manages the parts in diverse layouts.
[83]	vr	mill	assembly,content generation,operation	3dscan, hmd,pc	webxr	n	is, 5 participants (roles: experts, tutors, trainees).	The trainee’s actions are monitored and sent to the management system to execute the final evaluation.
[84]	ar,vr	mill	maintenance,tutoring,operation	ab, gl,mo,sg	fr	y	is, 20 operators (10 XR and 10 traditional training).	After training, the user performance was measured for two weeks, and XR workers outperformed traditionally trained ones.
[85]	mr	mill	collaborative, content generation, maintenance	cam,hol,pc	fr, mrtk,un,vf	n	as and is (operator and expert engineer).	Operators perform CNC machine maintenance based on expert instruction, using a cloud platform to share data.
[86]	mr	lcut	collaborative, content generation	mo, pc,ss	fr, gac,un	n	is, 20 participants.	Three collaborative services are provided for various industrial stages: designs, sales, and inspections.

CNC: carv = carving, drill = drilling, gr = grinding, lcut = laser cutting, lth = lathe or turning, mill = machine center or milling, wedm = wire electrical discharge machining. Hardware: 3dscan = 3d scanner, ab = armband, ad = Arduino, cam = camera, gl = glasses, hol = HoloLens, htc = htc hmd, jn = jetson nano, mo = mobile, moc = motion capture system, oc = oculus hmd, ocdk = oculus dk hmd, pc = computer, plc = programmable logic controller, sg = Samsung gear hmd, ss = sensors. Software: 3ds = 3d Max, bl = Blender, ct = CATIA, eon = EON Studio, fr = framework, gac = Google ARCore, lv = LabView, mrtk = mixed reality toolkit, ps = Photoshop, ros = real operating system, un = Unity, sw = SolidWorks, vb = Visual Basic, vf = Vuforia, vrml = virtual reality modeling language, webxr = web API for XR, - = not mentioned. Experimental settings: as = academic scenario, is = industrial scenario.

Table 5

Travel limits of the Hermle machine center for kinematics animation.

Command	Lower Limit	Upper Limit	Caution Zone	Lower Soft Limit	Upper Soft Limit
Y	0 mm	800 mm	0%	0 mm	800 mm
X	−400 mm	400 mm	0%	−400 mm	400 mm
Z	−550 mm	0 mm	0%	−550 mm	0 mm
A	130 deg	130 deg	0%	130 deg	130 deg
C	−360,000 deg	360,000 deg	0%	−360,000 deg	360,000 deg
DOOR	−550 mm	0 mm	0%	−550 mm	0 mm

References

1. Basile, V.; Tregua, M.; Giacalone, M. A three-level view of readiness models: Statistical and managerial insights on industry 4.0. Technol. Soc.; 2024; 77, 102528. [DOI: https://dx.doi.org/10.1016/j.techsoc.2024.102528]

2. Cimino, A.; Longo, F.; Mirabelli, G.; Solina, V.; Verteramo, S. An ontology-based, general-purpose and industry 4.0-ready architecture for supporting the smart operator (part ii–virtual reality case). J. Manuf. Syst.; 2024; 73, pp. 52-64. [DOI: https://dx.doi.org/10.1016/j.jmsy.2024.01.001]

3. Huerta-Torruco, V.A.; Hernandez-Uribe, O.; Cárdenas-Robledo, L.A.; Rodríguez-Olivares, N.A. Effectiveness of virtual reality in discrete event simulation models for manufacturing systems. Comput. Ind. Eng.; 2022; 168, 108079. [DOI: https://dx.doi.org/10.1016/j.cie.2022.108079]

4. Azevedo, A.; Almeida, A.H. Grasp the challenge of digital transition in smes—A training course geared towards decision-makers. Educ. Sci.; 2021; 11, 151. [DOI: https://dx.doi.org/10.3390/educsci11040151]

5. Cárdenas-Robledo, L.A.; Hernández-Uribe, Ó.; Reta, C.; Cantoral-Ceballos, J.A. Extended reality applications in industry 4.0.–A systematic literature review. Telemat. Inform.; 2022; 73, 101863. [DOI: https://dx.doi.org/10.1016/j.tele.2022.101863]

6. He, Z.; Sui, X.; Jin, G.; Cao, L. Progress in virtual reality and augmented reality based on holographic display. Appl. Opt.; 2019; 58, pp. A74-A81. [DOI: https://dx.doi.org/10.1364/AO.58.000A74] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30873963]

7. Roldán, J.J.; Crespo, E.; Martín-Barrio, A.; Peña-Tapia, E.; Barrientos, A. A training system for industry 4.0 operators in complex assemblies based on virtual reality and process mining. Robot. Comput.-Integr. Manuf.; 2019; 59, pp. 305-316. [DOI: https://dx.doi.org/10.1016/j.rcim.2019.05.004]

8. Izquierdo-Domenech, J.; Linares-Pellicer, J.; Orta-Lopez, J. Towards achieving a high degree of situational awareness and multimodal interaction with AR and semantic AI in industrial applications. Multimed. Tools Appl.; 2023; 82, pp. 15875-15901. [DOI: https://dx.doi.org/10.1007/s11042-022-13803-1]

9. Slater, M.; Gonzalez-Liencres, C.; Haggard, P.; Vinkers, C.; Gregory-Clarke, R.; Jelley, S.; Watson, Z.; Breen, G.; Schwarz, R.; Steptoe, W. et al. The ethics of realism in virtual and augmented reality. Front. Virtual Real.; 2020; 1, pp. 1-13. [DOI: https://dx.doi.org/10.3389/frvir.2020.00001]

10. Roullier, B.; McQuade, F.; Anjum, A.; Bower, C.; Liu, L. Automated visual quality assessment for virtual and augmented reality based digital twins. J. Cloud Comput.; 2024; 13, 51. [DOI: https://dx.doi.org/10.1186/s13677-024-00616-w]

11. Papadopoulos, T.; Evangelidis, K.; Evangelidis, G.; Kaskalis, T.H. Mixed reality and the internet of things: Bridging the virtual with the real. Adv. Eng. Softw.; 2023; 185, 103527. [DOI: https://dx.doi.org/10.1016/j.advengsoft.2023.103527]

12. Kang, D.; Lee, C.G.; Kwon, O. Pneumatic and acoustic suit: Multimodal haptic suit for enhanced virtual reality simulation. Virtual Real.; 2023; 27, pp. 1647-1669. [DOI: https://dx.doi.org/10.1007/s10055-023-00756-5]

13. Rauschnabel, P.A.; Felix, R.; Hinsch, C.; Shahab, H.; Alt, F. What is XR? Towards a framework for augmented and virtual reality. Comput. Hum. Behav.; 2022; 133, 107289. [DOI: https://dx.doi.org/10.1016/j.chb.2022.107289]

14. Cardoso, M.S.; Lepikson, H.A. Monitoring and Integration of CNC Machines in Advanced Manufacturing Environment for Predictive Operations. Proceedings of the IX Symposium Internacional de Innovation and Technology; São Paulo, Brasil, 25–27 October 2023; [DOI: https://dx.doi.org/10.5151/siintec2023-305819]

15. Lai, Y.-S.; Lin, W.-Z.; Lin, Y.-C.; Hung, J.-P. Development of surface roughness prediction and monitoring system in milling process. Eng. Technol. Appl. Sci. Res.; 2024; 14, pp. 12797-12805. [DOI: https://dx.doi.org/10.48084/etasr.6664]

16. But, A.; Scaticailov, S. Using CAM Software to Improve Productivity. Proceedings of the IOP Conference Series: Materials Science and Engineering; Pitesti, Romania, 22–24 May 2019; [DOI: https://dx.doi.org/10.1088/1757-899X/564/1/012055]

17. Gohari, H.; Hassan, M.; Shi, B.; Sadek, A.; Attia, H.; M’Saoubi, R. Cyber–physical systems for high-performance machining of difficult to cut materials in I5.0 era—A review. Sensors; 2024; 24, 2324. [DOI: https://dx.doi.org/10.3390/s24072324]

18. Sousa, V.F.C.; Silva, F.J.G.; Fecheira, J.S.; Lopes, H.M.; Martinho, R.P.; Casais, R.B.; Ferreira, L.P. Cutting forces assessment in CNC machining processes: A critical review. Sensors; 2020; 20, 4536. [DOI: https://dx.doi.org/10.3390/s20164536]

19. Jarosz, K.; Chen, Y.T.; Liu, R. Investigating the differences in human behavior between conventional machining and cnc machining for future workforce development: A case study. J. Manuf. Processes; 2023; 96, pp. 176-192. [DOI: https://dx.doi.org/10.1016/j.jmapro.2023.04.037]

20. Koleda, P. Innovation of CNC machining education at the faculty of technology. New Trends Issues Proc. Humanit. Soc. Sci.; 2020; 7, pp. 84-91. [DOI: https://dx.doi.org/10.18844/prosoc.v7i1.4870]

21. Soori, M.; Jough, F.K.G.; Dastres, R.; Arezoo, B. Sustainable CNC machining operations, a review. Sustain. Oper. Comput.; 2024; 5, pp. 73-87. [DOI: https://dx.doi.org/10.1016/j.susoc.2024.01.001]

22. Prasetya, F.; Fortuna, A.; Jalinus, N.; Refdinal, R.; Fajri, B.R.; Wulansari, R.E.; Primawati, P.; Andriani, W.; Samala, A.D.; Luthfi, A. et al. Revolutionizing CNC lathe education: Designing instructional media integrated using augmented reality technology. TEM J.; 2024; 13, pp. 1695-1701. [DOI: https://dx.doi.org/10.18421/TEM132-82]

23. Chaichana, C.; Wongkalasin, S.; Sanrutsadakorn, A. A learning platform for computer numerical control (CNC) laboratory. J. Comput. Sci.; 2022; 18, pp. 705-714. [DOI: https://dx.doi.org/10.3844/jcssp.2022.705.714]

24. Wibisono, G.; Wijanarka, B.S.; Theophile, H. The link and match between the competency of vocational high schools graduates and the industry on CAD/CAM and CNC. J. Pendidik. Teknol. Kejuru.; 2020; 26, pp. 26-34. [DOI: https://dx.doi.org/10.21831/jptk.v26i1.27932]

25. Ghobrial, M.; Seitier, P.; Lagarrigue, P.; Galaup, M.; Gilles, P. Effectiveness of machining equipment user guides: A comparative study of augmented reality and traditional media. Mater. Res. Proc.; 2024; 41, pp. 2320-2328. [DOI: https://dx.doi.org/10.21741/9781644903131-255]

26. Studer, K.; Lie, H.; Zhao, Z.; Thomson, B.; Turakhia, D.G.; Liu, J. An Open-ended System in Virtual Reality for Training Machining Skills. Proceedings of the CHI Conference on Human Factors in Computing Systems; Honolulu, HI, USA, 11–16 May 2024.

27. Eslami, A.M.; Rawat, K.S.; Asthana, C.B. Curriculum Alignment for Workforce Development in Advanced Manufacturing. Proceedings of the ASEE Annual Conference & Exposition; Baltimore, MD, USA, 25–28 June 2023; [DOI: https://dx.doi.org/10.18260/1-2--42853]

28. Ryan, M.; Wang, Y.; Xiao, Q.; Liu, R.; Zhang, Y. Immersive Virtual Reality Training with Error Management for CNC Milling Set-up. Proceedings of the International Manufacturing Science and Engineering Conference; West Lafayette, IN, USA, 27 June–1 July 2022; [DOI: https://dx.doi.org/10.1115/MSEC2022-85770]

29. Pramudya, I.; Andesta, D.; Hidayat, H. Safety application and health work (k3) at department of CNC lathe using hazard identification risk assessment and risk control (HIRARC) method (case study of pt. Swadaya Graha). J. Appl. Eng. Technol. Sci.; 2022; 4, pp. 318-324. [DOI: https://dx.doi.org/10.37385/jaets.v4i1.1114]

30. Tender, M.; Fuller, P.; Vaughan, A.; Demian, P.; Chow, V.; Silva, F.; Couto, J.P.; Santos, R. Lessons Learned from the Use of Virtual Reality for Occupational Safety and Health Training in Thames Tideway Tunnel. Proceedings of the Digital Transformation of Health and Safety in Construction; Porto, Portugal, 21–22 June 2023.

31. Mansoori, S.; Salari Koohfini, Z.; Ghasemali, M. A Comparison between the effectiveness of e-learning and blended learning in industrial training. Interdiscip. J. Virtual Learn. Med. Sci.; 2020; 11, pp. 46-53. [DOI: https://dx.doi.org/10.30476/ijvlms.2020.84352.1006]

32. Radovan, M.; Radovan, D.M. Harmonizing pedagogy and technology: Insights into teaching approaches that foster sustainable motivation and efficiency in blended learning. Sustainability; 2024; 16, 2704. [DOI: https://dx.doi.org/10.3390/su16072704]

33. Liu, L.; Li, W.; Chen, X. Exploration and realization about teaching experimental of CNC machine tool based on virtual simulation technology. Manuf. Technol.; 2023; 23, pp. 485-494. [DOI: https://dx.doi.org/10.21062/mft.2023.066]

34. Antonelli, D.; Christopoulos, A.; Laakso, M.-J.; Dagienė, V.; Juškevičienė, A.; Masiulionytė-Dagienė, V.; Mądziel, M.; Stadnicka, D.; Stylios, C. A virtual reality laboratory for blended learning education: Design, implementation and evaluation. Educ. Sci.; 2023; 13, 528. [DOI: https://dx.doi.org/10.3390/educsci13050528]

35. Mourtzis, D.; Zogopoulos, V.; Katagis, I.; Lagios, P. Augmented reality based visualization of CAM instructions towards Industry 4.0 paradigm: A CNC bending machine case study. Procedia CIRP; 2018; 70, pp. 368-373. [DOI: https://dx.doi.org/10.1016/j.procir.2018.02.045]

36. Sibanda, V.; Kanganga, M.; Sibanda, N. Bridging the Gap between Industry and Academia-The Essence of Virtual Reality in Skills Development and Learning Factories. Proceedings of the 13th Conference on Learning Factories; Reutlingen, Germany, 9–11 May 2023.

37. Ilić, M.P.; Păun, D.; Popović Šević, N.; Hadžić, A.; Jianu, A. Needs and performance analysis for changes in higher education and implementation of artificial intelligence, machine learning, and extended reality. Educ. Sci.; 2021; 11, 568. [DOI: https://dx.doi.org/10.3390/educsci11100568]

38. Abizar, H.; Ramdani, S.D.; Supriyatna, D. Operator competency model for mechanical engineering expertise. AIP Conf. Proc.; 2023; 2671, 060005. [DOI: https://dx.doi.org/10.1063/5.0114532]

39. Setiawan, A.; Pratama, H.; Setyadi, K.; Asih, E.K.; Yosephine, V.S. Automated Material Handling System and Inspection System Model for Flexible Manufacturing Systems Learning. Proceedings of the Second Asia Pacific International Conference on Industrial Engineering and Operations Management; Surakarta, Indonesia, 14–16 September 2021.

40. de Giorgio, A.; Monetti, F.M.; Maffei, A.; Romero, M.; Wang, L. Adopting extended reality? A systematic review of manufacturing training and teaching applications. J. Manuf. Syst.; 2023; 71, pp. 645-663. [DOI: https://dx.doi.org/10.1016/j.jmsy.2023.10.016]

41. Sorathiya, P.C.; Singh, S.A.; Desai, K.A. Mobile-based augmented reality (AR) module for guided operations of CNC surface roughness machine. Manuf. Lett.; 2023; 35, pp. 1255-1263. [DOI: https://dx.doi.org/10.1016/j.mfglet.2023.08.069]

42. Nguyen, H.T.; Olsson, R.; Haugen, Ø. Automatic synthesis of recurrent neurons for imitation learning from CNC machine operators. IEEE Open J. Ind. Electron. Soc.; 2024; 5, pp. 91-108. [DOI: https://dx.doi.org/10.1109/OJIES.2024.3363500]

43. Longo, F.; Padovano, A.; De Felice, F.; Petrillo, A.; Elbasheer, M. From “prepare for the unknown” to “train for what’s coming”: A digital twin-driven and cognitive training approach for the workforce of the future in smart factories. J. Ind. Inf. Integr.; 2023; 32, 100437. [DOI: https://dx.doi.org/10.1016/j.jii.2023.100437]

44. Scoble-Williams, N.; Mallon, D.; Cantrell, S.; Zanza, M.; Griffiths, M.; Poynton, S. How play and experimentation in digital playgrounds can drive human performance. 2024 Global Human Capital Trends; DeloiItte Insights: Mexico City, Mexico, 2024; Available online: https://www2.deloitte.com/content/dam/insights/articles/glob176836_global-human-capital-trends-2024/DI_Global-Human-Capital-Trends-2024.pdf (accessed on 28 June 2024).

45. Lv, J.; Tang, R.; Jia, S.; Liu, Y. Experimental study on energy consumption of computer numerical control machine tools. J. Clean. Prod.; 2016; 112, pp. 3864-3874. [DOI: https://dx.doi.org/10.1016/j.jclepro.2015.07.040]

46. Fu, C.H.; Liu, J.F.; Guo, Y.B.; Zhao, Q.Z. A comparative study on white layer properties by laser cutting vs. Electrical discharge machining of nitinol shape memory alloy. Procedia CIRP; 2016; 42, pp. 246-251. [DOI: https://dx.doi.org/10.1016/j.procir.2016.02.280]

47. Kaba, K.; Purnell, B.; Liu, Y.; Royall, P.G.; Alhnan, M.A. Computer numerical control (CNC) carving as an on-demand point-of-care manufacturing of solid dosage form: A digital alternative method for 3d printing. Int. J. Pharm.; 2023; 645, 123390. [DOI: https://dx.doi.org/10.1016/j.ijpharm.2023.123390]

48. Molenaar, J.M.; Ingrassia, D. Mastering Digitally Controlled Machines—Laser Cutters, 3D Printers, CNC Mills, and Vinyl Cutters to Make Almost Anything; Apress: Berkeley, CA, USA, 2023; pp. 256-269.

49. Wu, H.; Wang, X.; Deng, X.; Shen, H.; Yao, X. Review on design research in CNC machine tools based on energy consumption. Sustainability; 2024; 16, 847. [DOI: https://dx.doi.org/10.3390/su16020847]

50. Devagiri, J.S.; Paheding, S.; Niyaz, Q.; Yang, X.; Smith, S. Augmented reality and artificial intelligence in industry: Trends, tools, and future challenges. Expert Syst. Appl.; 2023; 207, 118002. [DOI: https://dx.doi.org/10.1016/j.eswa.2022.118002]

51. Baroroh, D.K.; Chu, C.H.; Wang, L. Systematic literature review on augmented reality in smart manufacturing: Collaboration between human and computational intelligence. J. Manuf. Syst.; 2021; 61, pp. 696-711. [DOI: https://dx.doi.org/10.1016/j.jmsy.2020.10.017]

52. Daling, L.M.; Schlittmeier, S.J. Effects of augmented reality-, virtual reality-, and mixed reality–based training on objective performance measures and subjective evaluations in manual assembly tasks: A scoping review. Hum. Factors; 2024; 66, pp. 589-626. [DOI: https://dx.doi.org/10.1177/00187208221105135] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35635107]

53. Kaplan, A.D.; Cruit, J.; Endsley, M.; Beers, S.M.; Sawyer, B.D.; Hancock, P.A. The effects of virtual reality, augmented reality, and mixed reality as training enhancement methods: A meta-analysis. Hum. Factors; 2021; 63, pp. 706-726. [DOI: https://dx.doi.org/10.1177/0018720820904229] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32091937]

54. Akpan, I.J.; Offodile, O.F. The role of virtual reality simulation in manufacturing in industry 4.0. Systems; 2024; 12, 26. [DOI: https://dx.doi.org/10.3390/systems12010026]

55. Rey-Becerra, E.; Barrero, L.H.; Ellegast, R.; Kluge, A. Improvement of short-term outcomes with VR-based safety training for work at heights. Appl. Ergon.; 2023; 112, 104077. [DOI: https://dx.doi.org/10.1016/j.apergo.2023.104077]

56. Egger, J.; Masood, T. Augmented reality in support of intelligent manufacturing–a systematic literature review. Comput. Ind. Eng.; 2020; 140, 106195. [DOI: https://dx.doi.org/10.1016/j.cie.2019.106195]

57. Soori, M.; Arezoo, B. Virtual machining systems for CNC milling and turning machine tools: A review. Int. J. Eng. Future Technol.; 2021; 18, pp. 56-104.

58. Yin, Y.; Zheng, P.; Li, C.; Wang, L. A state-of-the-art survey on augmented reality-assisted digital twin for futuristic human-centric industry transformation. Rob. Comput. Integr. Manuf.; 2023; 81, 102515. [DOI: https://dx.doi.org/10.1016/j.rcim.2022.102515]

59. Asad, U.; Khan, M.; Khalid, A.; Lughmani, W.A. Human-centric digital twins in industry: A comprehensive review of enabling technologies and implementation strategies. Sensors; 2023; 23, 3938. [DOI: https://dx.doi.org/10.3390/s23083938]

60. Chan, V.S.; Haron, H.; Isham, M.I.B.M.; Mohamed, F.B. VR and AR virtual welding for psychomotor skills: A systematic review. Multimed. Tools Appl.; 2022; 81, pp. 12459-12493. [DOI: https://dx.doi.org/10.1007/s11042-022-12293-5]

61. Hensen, B. A Systematic Literature Review of Mixed Reality Learning Approaches. Proceedings of the International Conference on Extended Reality; Salento, Italy, 6–9 September 2023; pp. 15-34. [DOI: https://dx.doi.org/10.1007/978-3-031-43404-4_2]

62. Van der Want, A.C.; Visscher, A.J. Virtual reality in preservice teacher education: Core features, advantages and effects. Educ. Sci.; 2024; 14, 635. [DOI: https://dx.doi.org/10.3390/educsci14060635]

63. Brancaccio, M.; Mirauda, D.; Patera, S.; Erra, U. Virtual reality laboratories in engineering blended learning environments: Challenges and opportunities. J. e-Learn. Knowl. Soc.; 2023; 19, pp. 34-49. [DOI: https://dx.doi.org/10.20368/1971-8829/1135825]

64. Rojas-Sánchez, M.A.; Palos-Sánchez, P.R.; Folgado-Fernández, J.A. Systematic literature review and bibliometric analysis on virtual reality and education. Educ. Inf. Technol.; 2023; 28, pp. 155-192. [DOI: https://dx.doi.org/10.1007/s10639-022-11167-5]

65. Lai, J.W.; Cheong, K.H. Adoption of virtual and augmented reality for mathematics education: A scoping review. IEEE Access; 2022; 10, pp. 13693-13703. [DOI: https://dx.doi.org/10.1109/ACCESS.2022.3145991]

66. Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E. et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. Syst. Rev.; 2021; 10, 89. [DOI: https://dx.doi.org/10.1186/s13643-021-01626-4] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33781348]

67. Klingenberg, C.O.; Borges, M.A.V.; do Vale Antunes, J.A., Jr. Industry 4.0: What makes it a revolution? A historical framework to understand the phenomenon. Technol. Soc.; 2022; 70, 102009. [DOI: https://dx.doi.org/10.1016/j.techsoc.2022.102009]

68. Krajčovič, M.; Gabajová, G.; Matys, M.; Furmannová, B.; Dulina, L. Virtual reality as an immersive teaching aid to enhance the connection between education and practice. Sustainability; 2022; 14, 9580. [DOI: https://dx.doi.org/10.3390/su14159580]

69. Hirt, C.; Spahni, M.; Kompis, Y.; Jetter, D.; Kunz, A. Virtual reality training platform for a computer numerically controlled grinding machine tool. Int. J. Mechatron. Manuf. Syst.; 2021; 14, pp. 1-17. [DOI: https://dx.doi.org/10.1504/IJMMS.2021.115460]

70. Kroupa, J.; Tuma, Z.; Kovar, J.; Singule, V. Virtual laboratory for study of construction of machine tools. MM Sci. J.; 2018; pp. 2503-2506. [DOI: https://dx.doi.org/10.17973/MMSJ.2018_11_2017100]

71. Ottogalli, K.; Rosquete, D.; Amundarain, A.; Aguinaga, I.; Borro, D. Flexible framework to model industry 4.0 processes for virtual simulators. Appl. Sci.; 2019; 9, 4983. [DOI: https://dx.doi.org/10.3390/app9234983]

72. Rogers, C.B.; El-Mounayri, H.; Wasfy, T.; Satterwhite, J. Assessment of stem e-learning in an immersive virtual reality (VR) environment. Comput. Educ. J.; 2017; 8, pp. 1-9.

73. Wasfy, H.M.; Wasfy, T.M.; Peters, J.; El-Mounayri, H.A. Automated online process training in a virtual environment. Comput. Educ. J.; 2013; 4, pp. 51-60.

74. Chen, L.W.; Tsai, J.P.; Kao, Y.C.; Wu, Y.X. Investigating the learning performances between sequence-and context-based teaching designs for virtual reality (VR)-based machine tool operation training. Comput. Appl. Eng. Educ.; 2019; 27, pp. 1043-1063. [DOI: https://dx.doi.org/10.1002/cae.22133]

75. Kao, Y.C.; Tsai, J.P.; Cheng, H.Y.; Chao, C.C. Development of a virtual reality wire electrical discharge machining system for operation training. Int. J. Adv. Manuf. Technol.; 2011; 54, pp. 605-618. [DOI: https://dx.doi.org/10.1007/s00170-010-2939-1]

76. Macías García, M.E.; Cortés Pérez, A.A.; Izaguirre Alegría, A.R. Cyber-physical labs to enhance engineering training and education. Int. J. Interact. Des. Manuf.; 2020; 14, pp. 1253-1269. [DOI: https://dx.doi.org/10.1007/s12008-020-00704-6]

77. Monroy Reyes, A.; Vergara Villegas, O.O.; Miranda Bojorquez, E.; Cruz Sanchez, V.G.; Nandayapa, M. A mobile augmented reality system to support machinery operations in scholar environments. Comput. Appl. Eng. Educ.; 2016; 24, pp. 967-981. [DOI: https://dx.doi.org/10.1002/cae.21772]

78. Tatić, D.; Tešić, B. The application of augmented reality technologies for the improvement of occupational safety in an industrial environment. Comput. Ind.; 2017; 85, pp. 1-10. [DOI: https://dx.doi.org/10.1016/j.compind.2016.11.004]

79. Samala, A.D.; Amanda, M. Immersive learning experience design (ILXD): Augmented reality mobile application for placing and interacting with 3d learning objects in engineering education. Int. J. Interact. Mob. Technol.; 2023; 17, pp. 22-35. [DOI: https://dx.doi.org/10.3991/ijim.v17i05.37067]

80. Lai, Z.H.; Tao, W.; Leu, M.C.; Yin, Z. Smart augmented reality instructional system for mechanical assembly towards worker-centered intelligent manufacturing. J. Manuf. Syst.; 2020; 55, pp. 69-81. [DOI: https://dx.doi.org/10.1016/j.jmsy.2020.02.010]

81. Chuang, T.J.; Tsai, Y.Y.; Smith, S. A visuo-haptic mixed reality manual milling training simulator. Virtual Real.; 2023; 27, pp. 2417-2430. [DOI: https://dx.doi.org/10.1007/s10055-023-00816-w]

82. Tzimas, E.G.; Vosniakos, C.; Matsas, E. Machine tool setup instructions in the smart factory using augmented reality: A system construction perspective. Int. J. Interact. Des. Manuf.; 2019; 13, pp. 121-136. [DOI: https://dx.doi.org/10.1007/s12008-018-0470-z]

83. Carretero, M.D.P.; García, S.; Moreno, A.; Alcain, N.; Elorza, I. Methodology to create virtual reality assisted training courses within the industry 4.0 vision. Multimed. Tools Appl.; 2021; 80, pp. 29699-29717. [DOI: https://dx.doi.org/10.1007/s11042-021-11195-2]

84. Longo, F.; Nicoletti, L.; Padovano, A. Smart operators in industry 4.0: A human-centered approach to enhance operators’ capabilities and competencies within the new smart factory context. Comput. Ind. Eng.; 2017; 113, pp. 144-159. [DOI: https://dx.doi.org/10.1016/j.cie.2017.09.016]

85. Mourtzis, D.; Siatras, V.; Angelopoulos, J. Real-time remote maintenance support based on augmented reality (AR). Appl. Sci.; 2020; 10, 1855. [DOI: https://dx.doi.org/10.3390/app10051855]

86. Wang, J.; Qi, Y. A Multi-User Collaborative AR system for industrial applications. Sensors; 2022; 22, 1319. [DOI: https://dx.doi.org/10.3390/s22041319] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35214221]

87. Lee, L.-K.; Wei, X.; Chui, K.T.; Cheung, S.K.S.; Wang, F.L.; Fung, Y.-C.; Lu, A.; Hui, Y.K.; Hao, T.; U, L.H. et al. A systematic review of the design of serious games for innovative learning: Augmented reality, virtual reality, or mixed reality?. Electronics; 2024; 13, 890. [DOI: https://dx.doi.org/10.3390/electronics13050890]

88. Martínez-Gutiérrez, A.; Díez-González, J.; Perez, H.; Araújo, M. Towards industry 5.0 through metaverse. Robot. Comput.-Integr. Manuf.; 2024; 89, 102764. [DOI: https://dx.doi.org/10.1016/j.rcim.2024.102764]

89. Mourtzis, D.; Angelopoulos, J. Development of an extended reality-based collaborative platform for engineering education: Operator 5.0. Electronics; 2023; 12, 3663. [DOI: https://dx.doi.org/10.3390/electronics12173663]

90. Dassault Systèmes, The 3DExperience Platform. Available online: https://www.3ds.com/3dexperience (accessed on 28 June 2024).

91. Lotti, G.; Villani, V.; Battilani, N.; Fantuzzi, C. New trends in the design of human-machine interaction for CNC machines. IFAC-PapersOnLine; 2019; 52, pp. 31-36. [DOI: https://dx.doi.org/10.1016/j.ifacol.2019.12.080]

92. Ntemi, M.; Paraschos, S.; Karakostas, A.; Gialampoukidis, I.; Vrochidis, S.; Kompatsiaris, I. Infrastructure monitoring and quality diagnosis in CNC machining: A review. CIRP J. Manuf. Sci. Technol.; 2022; 38, pp. 631-649. [DOI: https://dx.doi.org/10.1016/j.cirpj.2022.06.001]

93. Soori, M.; Arezoo, B.; Dastres, R. Machine learning and artificial intelligence in CNC machine tools, a review. Sustain. Manuf. Serv. Econ.; 2023; 2, 100009. [DOI: https://dx.doi.org/10.1016/j.smse.2023.100009]

94. Eirinakis, P.; Lounis, S.; Plitsos, S.; Arampatzis, G.; Kalaboukas, K.; Kenda, K.; Lu, J.; Rožanec, J.M.; Stojanovic, N. Cognitive digital twins for resilience in production: A conceptual framework. Information; 2022; 13, 33. [DOI: https://dx.doi.org/10.3390/info13010033]

95. Rahnama, H.; Alirezaie, M.; Pentland, A. A Neural-Symbolic Approach for User Mental Modeling: A Step Towards Building Exchangeable Identities. Proceedings of the AAAI Spring Symposium: Combining Machine Learning with Knowledge Engineering; Palo Alto, CA, USA, 22–24 March 2021.

96. Fernández-Caramés, T.M.; Fraga-Lamas, P. Forging the industrial metaverse-where industry 5.0, augmented and mixed reality, IIoT, opportunistic edge computing and digital twins meet. IEEE Access; 2024; 12, pp. 95778-95819. [DOI: https://dx.doi.org/10.1109/ACCESS.2024.3422109]

97. Chen, C.; Zhang, K.Z.; Chu, Z.; Lee, M. Augmented reality in the metaverse market: The role of multimodal sensory interaction. Internet Res.; 2024; 34, pp. 9-38. [DOI: https://dx.doi.org/10.1108/INTR-08-2022-0670]

98. Tang, Y.; Xu, J.; Liu, Q.; Hu, X.; Xue, W.; Liu, Z.; Lin, Z.; Zhang, Y.; Zhang, Z.; Ma, X. et al. Advancing haptic interfaces for immersive experiences in the metaverse. Device; 2024; 2, 100365. [DOI: https://dx.doi.org/10.1016/j.device.2024.100365]

99. Wang, Y.; Liang, J.; Yu, J.; Shan, Y.; Huang, X.; Lin, W.; Pan, Q.; Zhang, T.; Zhang, Z.; Gao, Y. et al. Multiscale haptic interfaces for metaverse. Device; 2024; 2, 100326. [DOI: https://dx.doi.org/10.1016/j.device.2024.100326]

100. Yang, Y.; Ma, M.; Van Doan, T.; Hulin, T.; Fitzek, H.F.; Nguyen, G.T. Touch the Metaverse: Demonstration of Haptic Feedback in Network-Assisted Augmented Reality. Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events; Biarritz, France, 11–15 March 2024; [DOI: https://dx.doi.org/10.1109/PerComWorkshops59983.2024.10503143]

101. Li, S.; Xie, H.L.; Zheng, P.; Wang, L. Industrial metaverse: A proactive human-robot collaboration perspective. J. Manuf. Syst.; 2024; 76, pp. 314-319. [DOI: https://dx.doi.org/10.1016/j.jmsy.2024.08.003]

102. Aruanno, B.; Carruba, M.C.; Mondellini, M.; Santos-Paz, J.A.; Ferrise, F.; Karaki, J.; Covarrubias, M. Enhancing inclusive education for young students with special needs through mixed reality: Exploring the potential of CNC milling machine application. Comput.-Aided Des. Appl.; 2024; 21, pp. 522-535. [DOI: https://dx.doi.org/10.14733/cadaps.2024.522-535]

103. James, S.; Eckert, G. A feasibility study on mixed reality-based visualization and interaction tool for performance improvement of metal cutting processes. Metals; 2023; 13, 286. [DOI: https://dx.doi.org/10.3390/met13020286]