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2024 | Book

Transforming Industry using Digital Twin Technology


About this book

​ This book enables readers with varying backgrounds to understand the need for Digital Twin technologies. The authors describe how digital twin amounts to the convergence of the physical and the virtual worlds where each industrial process, asset, service, or product gets a digital replica and dynamic digital blueprint and representation, from the design phase to the deployment phase. Through this book, readers will be enabled to work on Digital Twin techniques and gain from experience. The book will provide a high level of understanding of the emerging technologies and why Digital Twin offers the potential of acquiring and processing a tremendous amount of data from the physical world.

Table of Contents

Digital Twins in Industry: Real-World Applications and Innovations
A digital twin is a computer-generated clone of a physical entity, activity, or system that permits real-time simulation and assessment. It creates a precise model by merging data from sensors, devices, and numerous inputs to reflect the behavior and qualities of the underlying actual object or system. Manufacturing, aerospace, health, and energy industries use digital twins to improve operational optimization, increase efficiency, and reduce costs. Furthermore, these virtual equivalents are used in fields such as product design, predictive maintenance, and exploratory what-if analysis. This chapter delves into the numerous applications of digital twins across different areas.
Shamik Tiwari, Amar Shukla
Artificial Intelligence in Digital Twins for Sustainable Future
AI performs a good-sized position in the virtual dual era with the aid of presenting superior analytic and predictive modelling abilities. The digital dual era is an effective idea that entails growing a digital reproduction of a physical machine, which includes a product, process, or carrier. By leveraging AI, digital dual generation can assist companies to make higher choices, lessening prices, and improving overall performance. Digital twin refers to the digital replica or digital representation of a bodily entity, which includes a product, machine, or technique. The concept of the digital twin has won giant attention in current years, especially with the advancement of the Internet of Things (IoT), synthetic intelligence (AI), and other virtual technologies. One of the primary programs of AI in digital twin technology is predictive renovation. By reading real-time facts from sensors and different resources, AI algorithms can perceive styles and anomalies that suggest capability system disasters. These facts can be used to timetable preventive upkeep, averting highly priced downtime and maintenance. Another region in which AI can be beneficial in digital twin technology is simulation and optimization. AI algorithms can run simulations on virtual twins to explore specific scenarios and become aware of the most excellent answers. This can assist corporations to make extra informed choices, which include while to scale up manufacturing or while to introduce new merchandise. AI can also be used to beautify the accuracy of virtual twin models by using enhancing records evaluation and predictive modelling abilities. This can assist companies in better recognizing how their systems behave, identifying areas for improvement, and optimizing their operations for optimum efficiency. Overall, AI is a critical factor of virtual twin technology, allowing agencies to leverage the energy of records and analytic to enhance their operations and obtain higher effects.
Pranati Rakshit, Nandini Saha, Shibam Nandi, Pritha Gupta
The Place and Role of Digital Twin Applications: Directions for Energy and Education Sector
Today’s many challenges include low productivity, a lack of R&D, and poor technical advancements faced by many industries. Digital twins (DT) applications are used in both the energy and education sectors to tackle challenging problems. For instance, future complex power plants will need DT technology to achieve high availability, dependability, and ease of maintenance at lower costs. On the other hand, DT can help institutions create simulation models based on course requirements and make the ultimate immersive learning experience achievable. By using DT technologies in Augmented Reality (AR) or Virtual Reality (VR) simulated learning experiences, students can learn abstract concepts in their learning style. In this way, the employment of DT technologies in higher education can improve learning outcomes, enhance understanding, and promote student motivation for a better learning experience. The opportunity to combine the physical and digital worlds is provided by DT applications, and DT technology has the potential to address challenges in the energy and education sectors. As a result, the idea of DT draws a great deal of enthusiasm and is the step of development. The current chapter’s main aim is to thoroughly analyze the concepts, technologies, and applications of DT in the energy and education sectors. This study demonstrates that DT has a significant potential to address a range of issues in the energy and education sectors. As a result, the degree of awareness and necessity for the implementation of DT in the energy and educational sectors is raised by the current research.
Nurcan Kilinc-Ata, Ridvan Ata
The Role of Digital Trust in Enhancing Cyber Security Resilience
Digital trust and cyber-security are two of the most important aspects of modern technology. Digital trust is the confidence that users have in the security of their digital assets, such as data, applications, and networks. Cyber security is the practice of protecting these digital assets from malicious actors. Together, digital trust and cyber security are essential for protecting users from cyber threats and ensuring the safety of their digital assets. Digital trust is built through a combination of authentication, authorization, encryption, and other security measures. Encryption is the process of encoding data so that it can only be accessed by authorized users. These measures help to ensure that only authorized users can access digital assets and that data is kept secure. Cyber security is the practice of protecting digital assets from malicious actors. This includes preventing unauthorized access, detecting and responding to threats, and recovering from attacks. Cyber security measures include firewalls, antivirus software, intrusion detection systems, and other security tools. These measures help to protect digital assets from malicious actors and ensure that data is kept secure.
Praveen Kumar Malik
From Reactive to Proactive: Predicting and Optimizing Performance for Competitive Advantage
In today’s world, performance is the most important attribute that every living and non-living thing must possess to succeed. For a software product to meet its users’ performance expectations and requirements, performance prediction is critical. Software products are expected to perform well, and if they do not, it can result in user dissatisfaction, negative reviews, and lost revenue. The prediction of performance can be helpful to developers in identifying potential performance issues and optimizing the software product to meet user expectations. It can be time consuming and costly to resolve performance issues after the release of a product. Over the long term, the ability to predict performance can result in time and cost savings by detecting critical performance issues beforehand in the development process. The workload of software products must be able to scale as they grow, and more users use them. Performance is a key differentiator in today’s competitive software market. It is possible to gain a significant competitive advantage by providing a product that performs better than its competitors. The performance of a product needs to be predicted and optimized before it goes to market. Using robotic process automation, we will predict the performance of an application and identify bottlenecks, which can then be optimized.
Tapan Kumar Behera, Deep Manishkumar Dave
DT-AXYOLOV5: An Efficient Digital Twin–Assisted Deep-Learning-Based Blockchain Framework for Patient Discomfort Detection in Smart Healthcare System
Advancements in IT technologies have garnered vast numbers of data on healthcare activities, which helps doctors to diagnose diseases at low costs. Digital twin (DT) is a technology that imitates a physical entity to generate digital data representations. This technology becomes more powerful when it is combined with emerging technologies such as the Internet of Things, machine learning, and blockchain to monitor patients’ health. The proposed system contains a step-by-step process for recognizing patients’ diseases in the hospital through discomfort detection. First, the Azure Digital Twins Python package collects the video input of patients from the hospital by using an Internet protocol (IP) camera, which is preprocessed for machine-learning model AX-YOLOV5, using the AlphaPose library to recognize the 18 key points of human organs. The key points are used to identify the body position of a patient, either lying on a bed or sitting. The temporal thresholding technique recognizes health issues by how repeatedly the coordinates of the key points of the human body move within a certain period. Moreover, the coordinates of the key points are assessed for identifying the correct disease. Additionally, the blockchain-based practical Byzantine fault tolerance (pBFT) algorithm effectively stores and protects individuals’ healthcare data. Finally, the efficiency of the proposed system uses calculations that are based on detecting patient discomfort, model training and testing, latency, and data-processing cost. According to the experimental results, the proposed system’s efficacy rate for recognizing the disease can reach 98.3%.
J. Antony Vijay, C. D. Premkumar, P. Revathi
Smart Factory Digital Twin for Performance Measurement, Optimization, and Prediction
The fourth industrial revolution, also known as Industry 4.0, introduces the vision for a smart factory. A smart factory has highly flexible and efficient manufacturing processes that produce high quality products with minimum waste. A Digital Twin integrates the 3-D design model of a physical object such as a machine with the real-time data generated from that machine. Digital Twins are often used to monitor products while they are being actively used by customers. Instead, in this chapter, the focus is on the application of Digital Twin technology in manufacturing a product. The use of Digital Twin technology for performance management is applicable across different manufacturing processes including continuous, discrete, batch, additive and job shop. Traditionally, Manufacturing Execution System (MES) is used to start manufacturing and track manufacturing performance. This chapter explains why it makes sense to have the Digital Twin manage manufacturing performance. Universally, manufacturing performance is measured in terms of Overall Equipment Effectiveness (OEE). Increasing OEE allows manufacturers to produce more finished products with existing resources. The requirements and solution architecture for the Smart Factory Digital Twin (SFDT) for performance management is described in three parts: Measurement, Optimization, and Prediction. The first part is about the SFDT solution architecture for measuring the OEE and visualization. OEE visualization helps factory leadership understand the magnitude of availability, performance, speed, and yield losses. Next the SFDT solution architecture for Performance Optimization is explained. This includes the use of Statistical Process Control (SPC) and use of Augmented Reality (AR) applications. In terms of performance prediction, the use of SFDT for determining Remaining Useful Life (RUL) of physical objects such as machines and “What-If” analysis through simulation is explained. In summary, SFDT enables measuring, optimizing, and predicting the manufacturing performance of a smart factory. The discussion in this chapter is not tied to any commercial product or framework.
Suhas D. Joshi
Blockchain and Digital Twin
The convergence of Blockchain with Digital Twin technologies holds transformative potential for revolutionizing digital asset management and maintenance automation in businesses and large organizations. This transformative potential arises from the ability to establish secure and transparent digital representations of assets and processes. Digital Twin technology enables the creation of virtual replicas of physical objects, facilitating cost-effective testing, monitoring, and maintenance in a virtual environment for diverse processes and scenarios. However, challenges persist in ensuring the security, integrity, privacy, and traceability of sensitive data across Digital Twins, exposing them to cybersecurity threats. These challenges can be effectively mitigated by utilizing Blockchain technology with robust cryptographic mechanisms, ensuring data confidentiality, network-level security, traceability, and transparency. Decentralized storage infrastructure like IPFS enhances data sharing, storage, and access control across distributed sectors, while smart contracts automate processes, including updates and maintenance. This chapter tackles issues related to digital twins, such as scalability, interoperability, lack of standardization, and connectivity, proposing solutions leveraging Blockchain and cryptographic mechanisms. It explores potential applications and technical advantages in healthcare, manufacturing, and supply chain management, concluding with an overview of current research trends and future directions in this dynamic field.
Durga Vinay Balla, Sravya Sri Kadiyala, Nanda Kiran Kante
Personalize Learning Experience in Education Using Digital Twins with Human-Centered Design and Pedagogy
Digital technology plays a major role in all fields, specifically education, which creates a huge transformation for students, teachers, and researchers. The educational field needs an emerging technology called digital twin technology. It provides an exact replica of an object, machine, or progression. This technique provides an efficient teaching and learning process for both students and teachers. This chapter explains the digital twin technique usage for a better teaching and learning process by including human and pedagogy techniques. The proposed work will involve developing a new platform for learning and teaching needs. A digital twin is used by involving machine learning algorithms and data analytics to create a better path for teachers and students. This chapter mainly focuses on human-centered digital twin by considering ethics, environment, or society by incorporating a vast learning methodology such as virtual learning and experimental learning, which makes the students study interestingly. This chapter focuses on allowing both students and teachers to learn through hands-on or experience-based learning by interacting with digital twin technology with great experience. Furthermore, this chapter will concentrate on research in education.
A. Reethika, P. Kanaga Priya
Human Digital Twin Processes and their Future
In recent years, the concept of the human digital twin has emerged as a promising approach to revolutionize healthcare and unlock the full potential of precision medicine. By creating a virtual replica of an individual’s biological, physiological, and behavioral characteristics, a human digital twin can be used to develop personalized treatment plans, improve disease prevention, and discover genetics that contribute to health and disease. A human digital twin enables a deeper understanding of the complex interplay between environmental and lifestyle factors. It is used in various industries, such as aerospace and automotive, to optimize product design, predict performance, and improve maintenance. However, the application of this concept to human health is still in its early stages. Significant challenges remain to be overcome, and significant gains could be realized.
One of the key benefits of the human digital twin is the ability to integrate and analyze large numbers of data from various sources, such as electronic medical records, genomic information, wearable devices, and even social media. A comprehensive view of human health can identify early warning signs of disease, enabling timely intervention and prevention strategies. For example, digital twins can be used to predict an individual’s risk of developing diabetes on the basis of their genetic makeup, lifestyle, and environmental factors, allowing healthcare providers to tailor diet and exercise regimens to reduce that risk. Plans can be recommended.
The human digital twin could unlock the full potential of precision medicine and transform medicine as we know it. Digital twins provide comprehensive and dynamic representations of an individual’s health status to improve patient outcomes, provide personalized prevention and treatment, and broaden our understanding of the complex factors that influence health and disease. Strategies can be formulated. However, realizing these benefits will require overcoming significant technical, ethical, and regulatory challenges and fostering collaboration among researchers, healthcare providers, patients, and policymakers. The successful integration of the human digital twin into healthcare will undoubtedly pave the way for a new era of personalized, data-driven medicine that improves individual and collective health.
This chapter consists of seven sections. Section “Introduction” discusses the history, evolution, process, advantages, and disadvantages of human digital twin technologies. Section “HDT Process with Various Recent Technologies” deliberates about the integration of various recent technologies with human digital twin (HDTs). A conceptual paradigm for HDTs is described in section “Conceptual Paradigm for DT and HDT”. An HDT and a human being are compared in section “Human Digital Twin Technology and Human Beings”. The different types of wearable technologies featuring HDTs are categorized in section “Wearable Devices and HDTs”. Section “Development of Human Digital Twins in Healthcare” provides information on the development of HDTs in healthcare. Finally, section “Conclusion” concludes all other sections.
R. Hepziba Gnanamalar
Digital Twin Application in Various Sectors
The digital twin is an electronic representation of an actual or intended entity, process, or system (the physical twin) that serves as a digital counterpart that is effectively indistinguishable from the actual entity, system, or process. A digital twin has been envisioned from its inception as a premise for Product Lifecycle Management and as a tool used throughout the entire lifecycle of a physical entity (create, build, operate/support, and dispose). Due to the granularity of information, the digital twin representation depends on the use cases it is created for. It is possible and common for a digital twin to exist before a physical entity does. Simulating and modeling the intended entity’s lifecycle is possible with the use of a digital twin at the creation phase. A digital twin is a prototype to explore the issues of a real entity, it is synchronized with the corresponding real-time scenario to explore the challenges to be handled.
The purpose of digital twins is to create digital companions for physical objects using 3D modeling. In this way, physical objects can be projected into the digital world while displaying their status. For instance, when sensors gather data from connected devices, they can be used to update a “digital twin” replica of the device’s state in real time. This technology is alternatively referred to as “device shadow.” The physical properties of objects are an accurate and up-to-date representation of the physical object’s properties such as shape, movement, direction, appearance states, and position.
Monitoring, diagnostics, and prognostics can be performed using a digital twin to optimize asset performance. The prognostic outcome can be improved by combining predicted result, sensory data past data, experts data, and reinforcement learning. As a result, digital twins can be used to find the root cause of issues and improve productivity in complex prognostics and intelligent maintenance systems.
For the automotive application, digital twins of autonomous vehicles and their sensor suites incorporated into a path of travel have also been suggested as a way to remove the obstacles in development, testing, and validation, especially when artificial intelligence approaches are used to develop the algorithms, which require more training and validation on the data sets. Many industrial use cases can be supported by this technology, such as the manufacturing industry, urban planning and construction industry, healthcare industry, automotive industry, etc.
M. Mythily, Beaulah David, J. Antony Vijay
A Review of Digital Twin Applications in Various Sectors
The concept of digital twin (DT) has rapidly progressed from a theoretical concept to a practical application, with widespread adoption across multiple sectors. This chapter explores sectors like manufacturing, energy, healthcare, transportation, construction, aerospace industry, and smart cities where digital twin knowledge is being used and highlights its various applications. In the manufacturing sector, digital twins are employed to improve product quality, enhance production processes, and predict equipment failures. In the energy sector, digital twins enhance the efficiency of energy systems, predict maintenance needs, and reduce energy consumption. In healthcare, digital twins are used to create tailored patient models, simulate surgical procedures, and optimize treatment plans. In transportation, digital twins optimize logistics and reduce delivery times. In construction, they help improve project management, reduce errors, and enhance safety. In agriculture, digital twins optimize crop yields and resource management, enabling farmers to make more informed decisions about water usage, fertilizer application, and pest control. In the aerospace industry, digital twins monitor the performance of aircraft, predict maintenance needs, and improve safety. This technology reduces maintenance costs and enhances overall aircraft reliability. In smart cities, digital twins enhances various aspects of city life, such as controlling traffic flow, minimizing energy consumption, and improving public safety. Planners can test different scenarios and maximize resources for effective and environmentally friendly city living. While digital twin technology offers numerous benefits, its implementation requires a significant investment in infrastructure, data management, and expertise. These factors present challenges to widespread adoption. Despite the challenges, this chapter analyzes how digital twin technologies have the potential to revolutionize a variety of industries by giving real-time information, lowering costs, optimizing processes, and improving safety.
P. Kanaga Priya, A. Reethika
Digital Twin-Enabled Solution for Smart City Applications
A digital twin represents a virtual counterpart of a physical entity or process, capable of acquiring real-world data to mirror, validate, and emulate the current and future behavior of its physical counterpart. It holds significance in data-driven decision-making, intricate system monitoring, product validation, simulation, and lifecycle management across various domains, including industrial, automotive, medical, and smart cities. This systematic literature review aims to offer a comprehensive understanding of digital twin technology, exploring its implementation challenges and limitations in diverse engineering and related applications. With the increasing adoption of digital twins, particularly fueled by the growth of the Internet of Things (IoT), these virtual replicas find favor due to their affordability and user-friendly nature. The concept of smart cities exemplifies the application of digital twins, facilitating effective urban planning and land-use optimization. By enabling the modeling of plans before implementation, digital twins serve as a tool to identify and rectify flaws in designs before they manifest in reality. Architectural components such as housing, wireless network antennae, solar panels, and public transportation can benefit from digital twin techniques. This chapter includes a case study, providing additional insights into digital twin applications, making it a valuable resource for researchers in the field.
Pawan Whig, Balaram Yadav Kasula, Ashima Bhatnagar Bhatia, Rahul Reddy Nadikattu, Pavika Sharma
Combining Digital Twin Technology and Intelligent Transportation Systems for Smart Mobility
Digital twin technology and intelligent transportation systems (ITSs) are two related technologies that are transforming the transportation industry. Digital twin technology is a virtual replica of a physical object or system, such as transportation infrastructure or a vehicle. It allows for the real-time supervision or monitoring, analysis, and reconstruction of the work behavior of a physical system. In transportation, a digital twin can be used to model and monitor traffic flow, vehicle performance, road conditions, and other factors that impact the transportation system. The Internet of Things (IoT) is a collection of millions of interconnected devices that work seamlessly with each other. Its rapid emergence and evolution are two of the main factors that have raised the bar for creating innovative applications. In the digital era, artificial intelligence and the Internet of Everything (IoE), which are based on IoT, can enhance the work–life balance of users and other people. It can also be used to optimize the use of their resources. Intelligent transportation systems (ITSs) with IoT-enabled devices can be used to reduce traffic congestion. An ITS can be defined as a transportation system that has been improved thanks to its use of various technologies. The major purposes of an intelligent transportation system (ITS) are to evaluate and create analytical work and integrate a system with new technologies or innovations to achieve the target of traffic efficiency, improve environmental factors, save energy, save time, and ensure safety and security. This framework allows for the creation and implementation of the various components of a big data system and integrates them into the IoE or sensors. This case study describes the various components of the Internet of Things platform, where these components allow for a flexible and dynamic setup.
Ajay Dureja, Aman Dureja, Varun Kumar, Sachin Sabharwal
Navigating the Digital Landscape: Challenges of BIM and Digital Twin Adaptation
Digitization has been gaining tremendous popularity recently; construction sectors like any other industry have been exploiting digital transformation to attain more efficiency and optimization. These technologies are steadily developing and upgrading and have proven to be very beneficial. However, utilizing these Building Information Management tools and techniques poses its challenges and obstacle. The design and construction companies involved may find themselves persistently adapting to this ever-evolving paradigm shift, and these changes can be altering the traditional practices of project delivery systems. This chapter’s core objective is to realize these impacts and underline the level of adaptability required for successful integration of BIM and DT in constructions processes. The research firstly conducts an extensive literature review of current applications, benefits, key success factors, and challenges related to the successful integration of BIM. Thereafter, a qualitative analysis is employed where surveys and interviews are collected from around the globe, to develop a deeper understanding of the impact of this transformation. The key findings reveal that the benefits of digitization are enormous and lead to leaner construction; however, there are critical factors that account for a successful implementation. Although cumbersome, adapting to these new norms is essential for optimizing construction processes provided the required recourses can be acquired.
Kamal Jaafar, Mohamed Awais
Transforming Industry using Digital Twin Technology
Ashutosh Mishra
May El Barachi
Manoj Kumar
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