Figure 3 - uploaded by Satish Kumar V C
Content may be subject to copyright.
Source publication
The Fourth Industrial Revolution drives industries from traditional manufacturing to the smart manufacturing approach. In this transformation, existing equipment, processes, or devices are retrofitted with some sensors and other cyber-physical systems (CPS), and adapted towards digital production, which is a blend of critical enabling technologies....
Context in source publication
Context 1
... the proper integration of devices with their virtual replicas in the cyber-physical domain, and the effective exchange of information and data among digital twins, physical twins, and the outside world, Ref. [44] proposed a six-layer DT architecture, as shown in Figure 3. The six-layer DT architecture is an extension of the 5C architecture [45]. ...
Citations
... Although the current body of literature thoroughly examines the combination of hybridized machine learning algorithms with smart production and emphasizes their potential advantages for social sustainability, there is a noticeable lack of quantitative measures. A significant number of research studies have mostly provided descriptive or qualitative explanations, creating an opportunity to establish established benchmarks and quantifiable results (Tayal et al., 2020;Warke et al., 2021). The formulation of these quantitative frameworks is important because it allows for an unbiased evaluation of the actual effects of these technologies on social sustainability objectives in the manufacturing industry. ...
Industry 4.0 has ushered in a new era of technological advancements, particularly in smart production, using technologies like the Internet of Things, big data analytics, and artificial intelligence. While much attention has been focused on the technological and economic aspects of this transformation, the concept of social sustainability within smart production remains underexplored. This paper explores the intersection of technology and social sustainability in the context of smart production in China. Machine learning, especially in hybrid models, is examined as a tool to integrate social sustainability into smart production. These algorithms can analyze vast datasets, predict social disruptions, inform policymaking, and tailor technological solutions. The paper presents a comprehensive analysis of the performance of various machine learning models in forecasting solar PV energy production, with a focus on different photovoltaic technologies and emission scenarios. The results highlight the robustness of certain photovoltaic technologies, such as p-Si and m-Si, in the face of climate variability. The study introduces the MLP-CARIMA-GPM model as a benchmark in predicting solar PV energy output, challenging the traditional belief that composite models always offer superior results. Theoretical and policy implications are discussed, emphasizing the importance of aligning solar PV energy production with Sustainable Development Goals. The research underscores the pivotal role of sophisticated, hybrid machine learning models in ensuring sustainable energy production and offers valuable insights for policymakers, industry leaders, and stakeholders navigating the challenges of energy demands, climate change, and technological advancements. This study serves as a roadmap for achieving sustainable smart production, where technology and sustainability coalesce to illuminate possibilities for the future.
... [38]. The most cited topics within this cluster include "artificial intelligence" (110), "manufacturing process" (109), and "machine learning" (71). The fifth largest cluster #4 includes 71 members with a silhouette value of 0.647 and is labeled as "energy efficiency" by LLR. ...
... Cluster #3, ranked fourth in size, comprises 41 members with a silhouette value of 0.668 and is categorized under smart manufacturing by LLR. Its principal reference is Warke, V (2021.0-JAN)[110] in Sustainability (Switzerland). The fifth largest cluster, #4, contains 16 members with a silhouette value of 0.909 and is designated as a future research direction by LLR. ...
Industry 4.0 is a new era of digitization that integrates digital technologies to create smart, interconnected production systems, marking a paradigm shift in different domains. One of the key technologies in this era is 'Digital Twin', which offers virtual representations of physical assets and processes. It is crucial to analyze the role of digital twin technology in the context of Industry 4.0 using scientometric analysis with citeSpace, a powerful tool for visualizing and analyzing citation networks. This research focuses on the growth trajectory of digital twin literature, identifies influential authors, institutions, and journals, and analyzes the geographical distribution of research contributions from the Scopus database for 2018-2024. The findings reveal insights into the current state of digital twin research, its impact on Industry 4.0, and opportunities for future exploration and collaboration in this rapidly evolving field.
... The basis for cloud architecture is cloud computing. This involves centralised computing services (e.g., storage, computing power) being made available via the Internet [56,63,67]. To this end, data are transferred from a local network to an external network via the internet so that data processing and storage takes place on servers there. ...
... The maximum achievable computing and storage capacities are usually very extensive [63]. Even if the use of cloud services incurs fewer or no initial investment costs [67], the booking of external services by providers incurs ongoing costs, which increase with growing performance requirements. Data transfer costs in particular can quickly become very expensive [63,68]. ...
The integration of Digital Twins (DTs) is becoming increasingly important in various industries. This entails the need for a comprehensive and practical IT infrastructure framework. This paper presents a modifiable medium-level architecture that serves as a link between established reference architectures such as RAMI 4.0 and the pragmatic implementation of Digital Twins. The functionalities of an IT infrastructure are considered, and functional hardware and software components for fulfilling these are described. The proposed architecture is suitable for various deployment scenarios, including local, cloud and hybrid cloud–edge configurations. In order to improve the applicability of the medium-level architecture, a step-by-step procedure is also proposed, which helps to transfer the overarching requirements for a Digital Twin into a suitable IT infrastructure. Finally, the results are demonstrated by an exemplary application to a two-stage industrial gearbox.
... Digital twinning in manufacturing has a wide range of possible applications, such as remote asset control, simulation, and asset monitoring using virtual objects. Additionally, by better understanding customers' wants, creating improvements for casaaasprocesses, services, and products, and stimulating new company innovation, digital twin technology can help manufacturing increase customer happiness [46]. These developments aided in the accurate application of Digital Twin for process optimization and real-time monitoring [35,38]. ...
... These developments aided in the accurate application of Digital Twin for process optimization and real-time monitoring [35,38]. The significant technological progress that Digital Twins have made over the past four decades is demonstrated positively, has been recorded [46]. Digital twinning in manufacturing has a wide range of possible applications, such as remote asset control, simulation, and asset monitoring using virtual objects. ...
... Digital twinning in manufacturing has a wide range of possible applications, such as remote asset control, simulation, and asset monitoring using virtual objects. Additionally, by better understanding customers' wants, creating improvements for current processes, services, and products, and stimulating new company innovation, digital twin technology can help manufacturing increase customer happiness [46]. ...
A digital twin refers to a virtual copy of an entity, which can be a system, process, or product. It is updated using current data and helps bridge the gap between the real and digital worlds. By creating an identical replica of an object, such as a locomotive, chair, or skyscraper, sensors are attached to collect real-time data, which is then translated into a virtual representation. The digital twin is created, examined, and built in a virtual environment. This technology has been advanced by the development of the Internet of Things (IoT), which allows for constant connectivity and real-time data transmission. The digital twin is used to anticipate the future and comprehend the present. This chapter discusses and explains several digital twin applications and the value of digital twins.
... However, digital twins can also be used in enhancing security and resilience, given their capability to identify malicious activities within a system. In the past, some [37,47]. ...
... Digital twins are relatively new and emerging technologies that have low maturity; therefore, they are accompanied by numerous challenges such as security, privacy, trust and data quality, safety, and missing standardization [16,47]. The focus of this paper is to discuss the challenges related to security, privacy, trust, and safety. ...
... When a digital twin is implemented, it effectively doubles the attack surface as either the physical asset or the digital twin can be used as access points [3,6]. When digital twins are used with IT, they can increase the threats related to IT/OT integration by making the OT systems even more exposed and connected [47]. It is important to be aware of the threat of digital twins being connected to various IoT devices, which may inherit vulnerabilities [36,47]. ...
The health sector is a critical and vulnerable infrastructure, making it an easy target for hackers and attackers. Healthcare is also a highly trusted sector that contains sensitive and personal information; therefore, exploiting its vulnerabilities can lead to great financial and political gains. The digital twin is an emerging technology that could play a powerful role in the healthcare sector in the future, for example, offering customized diagnosis and treatment to each patient. Although digital twins come with several advantages, there are several challenges to implementing digital twins successfully in healthcare. In this paper, we have discussed the different challenges related to the security, privacy, trust, and safety of digital twins and its implementation in healthcare. We have also presented the comparative conflict analysis of implementing the security, safety, privacy, trust, and operational requirements for IoT digital twins.
... Dari tahun 2020 hingga 2025, smart manufacturing diperkirakan mengalami pertumbuhan dengan nilai Compound Annual Growth Rate (CAGR) sebesar 12,4% [11]. Hal ini menunjukkan bahwa tren penggunaan transformasi digital semakin meningkat. ...
Digital Twin (DT) is a virtual model of a process, product, and/or service that applies the concept of modeling and simulation, which is one of the digital transformation solutions in Industry 4.0. To create a sustainable manufacturing industry, DT plays an important role in integrating automation systems with intelligent elements and digital technology in various aspects of production. Therefore, this paper explains the role of DT which focuses on manufacturing automation by considering aspects of sustainable development. A systematic literature review method is used to obtain more comprehensive information from previous research findings. The study found that DT plays a role in improving productivity and manufacturing operations. Based on the concept of sustainability, DT plays a role in improving the efficiency of energy use and asset management. The results of this study can help policymakers in the manufacturing industry make quick and accurate decisions for the sustainability of their companies. Abstrak Digital Twin (DT) adalah model virtual dari sebuah proses, produk, dan/atau layanan dengan menerapkan konsep pemodelan dan simulasi, yang merupakan salah satu solusi transformasi digital di Industri 4.0. Untuk menciptakan industri manufaktur yang berkelanjutan, DT berperan penting dalam mengintegrasikan sistem otomasi dengan elemen cerdas dan teknologi digital dalam berbagai aspek produksi. Oleh karena itu, makalah ini menjelaskan peran DT yang berfokus pada otomatisasi manufaktur dengan mempertimbangkan aspek pembangunan berkelanjutan. Metode tinjauan literatur yang sistematis digunakan untuk mendapatkan informasi yang lebih komprehensif dari hasil penelitian sebelumnya. Penelitian ini menemukan bahwa DT berperan dalam meningkatkan produktivitas dan operasi manufaktur. Berdasarkan konsep keberlanjutan, DT berperan dalam meningkatkan efisiensi penggunaan energi dan manajemen aset. Hasil penelitian ini dapat membantu para pengambil kebijakan di industri manufaktur dalam mengambil keputusan yang cepat dan tepat untuk keberlanjutan perusahaannya. Kata kunci: Digital Twin, Manufaktur, Sistem otomasi, Pembangunan berkelanjutan Pendahuluan Akselerasi Revolusi Industri 4.0 yang dipicu oleh kebijakan strategis telah mengakibatkan banyak industri manufaktur di seluruh dunia berusaha meningkatkan nilai bisnis dan efisiensi operasional melalui transformasi digital pada seluruh rantai bisnisnya. Kemajuan teknologi informasi dan komunikasi (TIK) telah membantu perkembangan manufaktur dengan cepat. Hal ini mendorong kolaborasi teknologi otomatisasi dengan elemen cerdas dan teknologi digital. Penerapan konsep ini berpusat pada sistem otomasi yang didukung dengan penggunaan computer-aided (CAD, CAM, CAE, FEA, dll) serta Internet of Things (IoT), Big Data, Artificial Intelligence (AI), Cloud computing, dan Aditive Manufacturing [1].
... DT applications in positions and supply chain management, especially DT's role in operations traceability, transport maintenance, remote assistance, asset visualization, and design customization, are reviewed in several publications [11,15,16,30,42,[66][67][68][69][70]). DT applications in positions and supply chain management, especially DT' role in operations traceability, transport maintenance, remote assistance, asset visualization, and design customization, are reviewed in several publications [11,15,16,30,42,[66][67][68][69][70]). ...
... DT applications in positions and supply chain management, especially DT's role in operations traceability, transport maintenance, remote assistance, asset visualization, and design customization, are reviewed in several publications [11,15,16,30,42,[66][67][68][69][70]). DT applications in positions and supply chain management, especially DT' role in operations traceability, transport maintenance, remote assistance, asset visualization, and design customization, are reviewed in several publications [11,15,16,30,42,[66][67][68][69][70]). ...
The Industrial Internet of Things (IIoT) and Digital Twins (DTs) are changing how digital models and physical products interact. IIoT connects to intelligence in the physical world, and DTs virtually represent their physical counterparts. As a result, DTs will be invaluable for testing and simulating new parameters and design variants. However, Despite the undeniable potential of DTs, they still cannot differentiate themselves from simulation technologies, and their application and adoption remain limited. This study defines the concept, highlights the evolution and development of DTs, reviews its key enabling technologies, identifies IIoT’s role as the backbone of DTs, examines DTs trends, highlights the key challenges, and explores its applications in the manufacturing process and Industry 4.0.
... Maintenance is a pivotal determinant that exerts a significant economic impact on the sector, garnering notable emphasis in the era of digitalization. The entire manufacturing cost is projected to include maintenance expenses ranging from around 15% to 40% [2]. Based on the U.S. Department of Energy findings, it has been shown that predictive maintenance offers cost savings of around 8-12% compared to preventive maintenance and can yield savings of up to 40% compared to reactive maintenance [3]. ...
... Digital twin general network architecture[2]. ...
One of the core ideas of Industry 4.0 has been the use of Digital Twin Networks (DTN). DTN facilitates the co-evolution of real and virtual things through the use of DT modelling, interactions, computation, and information analysis systems. The DT simulates product lifecycles to forecast and optimizes manufacturing systems and component behaviour. Industry and Academia have been developing Digital Twin (DT) technology for real-time remote monitoring and control, transport risk assessment, and intelligent scheduling in the smart industry. This study aims to design and simulate a comprehensive digital twin model connecting three factories to a single server. It incorporates remote network control, IoT integration, advanced networking protocols, and security measures. The model utilizes the Open Shortest Path First (OSPF) routing protocol for seamless network connectivity within the interconnected factories. Authentication, authorization, and accounting (AAA) mechanisms ensure secure access and prevent unauthorized entry. The Digital Twin Model is simulated using Cisco Packet Tracer, validating its functionality in network connectivity, security, remote control, and motor efficiency monitoring. The results demonstrate the successful integration and operation of the model in smart industries. The networked factories exhibit improved operational efficiency, enhanced security, and proactive maintenance.
... Maintenance is a pivotal determinant that exerts a significant economic impact on the sector, garnering notable emphasis in the era of digitalization. The entire manufacturing cost is projected to include maintenance expenses ranging from around 15% to 40% [2]. Based on the U.S. Department of Energy findings, it has been shown that predictive maintenance offers cost savings of around 8-12% compared to preventive maintenance and can yield savings of up to 40% compared to reactive maintenance [3]. ...
... Digital Twin General Network Architecture[2]. ...
One of the core ideas of Industry 4.0 has been the use of Digital Twin Networks (DTN). DTN facilitates the co-evolution of real and virtual things through the use of DT modelling, interactions , computation, and information analysis systems. The DT simulates product lifecycles to forecast and optimizes manufacturing systems and component behavior. Industry and Academia have been developing Digital Twin (DT) technology for real-time remote monitoring and control, transport risk assessment, and intelligent scheduling in the smart industry. This study aims to design and simulate a comprehensive digital twin model connecting three factories to a single server. It incorporates remote network control, IoT integration, advanced networking protocols, and security measures. The model utilizes the Open Shortest Path First (OSPF) routing protocol for seamless network connectivity within the interconnected factories. Access Control List (ACL) and Authenti-cation, authorization, and accounting (AAA) mechanisms ensure secure access and prevent unauthorized entry. The Digital Twin Model is simulated using Cisco Packet Tracer, validating its func-tionality in network connectivity, security, remote control, and motor efficiency monitoring. The results demonstrate the successful integration and operation of the model in smart industries. The networked factories exhibit improved operational efficiency, enhanced security, and proactive maintenance.
... The most important sustainable solutions were Energy Saving, Environmental Conservation, Increased Safety, Quality Improvement, End of Waste, Infrastructure, and Cost Reduction [21,27,33,[40][41][42][43][44][45][46]48,49]. ...
This paper addresses the concept of Sustainability 4.0 in services, which can be defined as an integrated approach that seeks to balance the dimensions of the triple bottom line (economic, social, and environmental factors) using Industry 4.0, enabling technologies to improve organizational processes. This paper aims to identify the contextual relationships between the sustainable solutions of I4.0 based on the principles and pillars of Industry 4.0 in services while using Interpretive Structural Modeling (ISM). The ISM model, composed of 16 sustainable solutions, was developed based on the vision of a law firm manager and validated by 19 experts. As a result, the model presented a six-level hierarchy for sustainable solutions and classified sustainable solutions for law firms as Dependent Sustainable Solutions, Liaison Sustainable Solutions, and Independent Sustainable Solutions. Moreover, this study highlights the importance of sustainable solutions in Industry 4.0 in services, raising awareness of the need for sustainable practices in organizations. Therefore, this research contributes to the advancement of scientific knowledge, offers practical guidance for law firm managers, and promotes sustainability in Industry 4.0 in services, benefiting both academia and society.
