The implementation of virtual reality in digital factory—a comprehensive review

Sekaran, Sivadas Chandra; Yap, Hwa Jen; Musa, Siti Nurmaya; Liew, Kan Ern; Tan, Chee Hau; Aman, Atikah

doi:10.1007/s00170-021-07240-x

Cited by 31 publications

(14 citation statements)

References 85 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The overview of managerial-focused applications of AR is presented in [ 165 ]. In addition, the literature reviews focus on AR implementation in robotics (see, e.g., [ 166 ]), supply chain management (see, e.g., [ 167 ]), shipbuilding industry (see, e.g., [ 168 ]), and smart manufacturing (see, e.g., [ 169 , 170 , 171 , 172 ]). The AI technologies implementation possibilities in AR are reviewed in [ 173 ].…”

Section: Systematic Literature Review Of the Selected Papers Within T...mentioning

confidence: 99%

Maintenance Performance in the Age of Industry 4.0: A Bibliometric Performance Analysis and a Systematic Literature Review

Werbińska-Wojciechowska

Winiarska

2023

Sensors

View full text Add to dashboard Cite

Recently, there has been a growing interest in issues related to maintenance performance management, which is confirmed by a significant number of publications and reports devoted to these problems. However, theoretical and application studies indicate a lack of research on the systematic literature reviews and surveys of studies that would focus on the evolution of Industry 4.0 technologies used in the maintenance area in a cross-sectional manner. Therefore, the paper reviews the existing literature to present an up-to-date and content-relevant analysis in this field. The proposed methodology includes bibliometric performance analysis and a review of the systematic literature. First, the general bibliometric analysis was conducted based on the literature in Scopus and Web of Science databases. Later, the systematic search was performed using the Primo multi-search tool following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The main inclusion criteria included the publication dates (studies published from 2012–2022), studies published in English, and studies found in the selected databases. In addition, the authors focused on research work within the scope of the Maintenance 4.0 study. Therefore, papers within the following research fields were selected: (a) augmented reality, (b) virtual reality, (c) system architecture, (d) data-driven decision, (e) Operator 4.0, and (f) cybersecurity. This resulted in the selection of the 214 most relevant papers in the investigated area. Finally, the selected articles in this review were categorized into five groups: (1) Data-driven decision-making in Maintenance 4.0, (2) Operator 4.0, (3) Virtual and Augmented reality in maintenance, 4) Maintenance system architecture, and 5) Cybersecurity in maintenance. The obtained results have led the authors to specify the main research problems and trends related to the analyzed area and to identify the main research gaps for future investigation from academic and engineering perspectives.

show abstract

Section: Systematic Literature Review Of the Selected Papers Within T...mentioning

confidence: 99%

Maintenance Performance in the Age of Industry 4.0: A Bibliometric Performance Analysis and a Systematic Literature Review

Werbińska-Wojciechowska

Winiarska

2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Objectives of digital factor; based on the information provided in Refs. (Bicocchi et al , 2019; Bloch et al , 2016; Chandra Sekaran et al , 2021; Horbach et al , 2011; Mishima et al , 2010)…”

Section: Figurementioning

confidence: 99%

“…In DF, digital technologies are used for visualization, defining mathematical models, business flows and data management techniques to encompass information from all aspects of a production (Agrawal et al , 2011). As the DF is described as the first step toward achieving IR 4.0, establishing and simplifying its requirements and aims has emerged as a critical study issue (Chandra Sekaran et al , 2021). Digital data management is one of the driving forces behind IR; however, it is not properly used in IR 3.0, which highlighted IT and computerization to expedite production methods.…”

Section: Development Of a Digital Factorymentioning

confidence: 99%

“…The distributed nature of DF envisions scalability, and easy integrity due to its peer-to-peer adaptation model. Furthermore, it should maintain a data repository for reconfigurable, modeling and forecasting a complicated and vibrant trend across the product life cycle (Chandra Sekaran et al , 2021).…”

Section: Development Of a Digital Factorymentioning

confidence: 99%

“…Although PLCM systems are simple to use, they are not linked to other systems such as data acquisition or controls (Cheng et al, 2018). As most of the PLCMs are developed in this (Bicocchi et al, 2019;Bloch et al, 2016;Chandra Sekaran et al, 2021;Horbach et al, 2011;Mishima et al, 2010) manner, they fail to operate in a heterogeneous industrial environment where data comes from a variety of devices, sectors, views and phases with varying levels of granularity (Shariatzadeh et al, 2016). Their lack of compatibility limits integration and data transfer between them and other devices.…”

Section: Digital Factory Challengesmentioning

confidence: 99%

See 2 more Smart Citations

A state-of-the-art digital factory integrating digital twin for laser additive and subtractive manufacturing processes

Tariq

Joy

et al. 2023

RPJ

View full text Add to dashboard Cite

Purpose This study aims to discuss the state-of-the-art digital factory (DF) development combining digital twins (DTs), sensing devices, laser additive manufacturing (LAM) and subtractive manufacturing (SM) processes. The current shortcomings and outlook of the DF also have been highlighted. A DF is a state-of-the-art manufacturing facility that uses innovative technologies, including automation, artificial intelligence (AI), the Internet of Things, additive manufacturing (AM), SM, hybrid manufacturing (HM), sensors for real-time feedback and control, and a DT, to streamline and improve manufacturing operations. Design/methodology/approach This study presents a novel perspective on DF development using laser-based AM, SM, sensors and DTs. Recent developments in laser-based AM, SM, sensors and DTs have been compiled. This study has been developed using systematic reviews and meta-analyses (PRISMA) guidelines, discussing literature on the DTs for laser-based AM, particularly laser powder bed fusion and direct energy deposition, in-situ monitoring and control equipment, SM and HM. The principal goal of this study is to highlight the aspects of DF and its development using existing techniques. Findings A comprehensive literature review finds a substantial lack of complete techniques that incorporate cyber-physical systems, advanced data analytics, AI, standardized interoperability, human–machine cooperation and scalable adaptability. The suggested DF effectively fills this void by integrating cyber-physical system components, including DT, AM, SM and sensors into the manufacturing process. Using sophisticated data analytics and AI algorithms, the DF facilitates real-time data analysis, predictive maintenance, quality control and optimal resource allocation. In addition, the suggested DF ensures interoperability between diverse devices and systems by emphasizing standardized communication protocols and interfaces. The modular and adaptable architecture of the DF enables scalability and adaptation, allowing for rapid reaction to market conditions. Originality/value Based on the need of DF, this review presents a comprehensive approach to DF development using DTs, sensing devices, LAM and SM processes and provides current progress in this domain.

show abstract

Insight Review on Advanced Digital Manufacturing Technology Solutions for Industry 4.0

Ponkumar,

Saravanan,

Cheriyan

et al. 2024

Knowledge Management and Industry Revolution 4.0

View full text Add to dashboard Cite

The implementation of virtual reality in digital factory—a comprehensive review

Cited by 31 publications

References 85 publications

Maintenance Performance in the Age of Industry 4.0: A Bibliometric Performance Analysis and a Systematic Literature Review

Maintenance Performance in the Age of Industry 4.0: A Bibliometric Performance Analysis and a Systematic Literature Review

A state-of-the-art digital factory integrating digital twin for laser additive and subtractive manufacturing processes

Insight Review on Advanced Digital Manufacturing Technology Solutions for Industry 4.0

Contact Info

Product

Resources

About