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Digital twins are models that emulate the operations, conduct, and execution of physical products, systems, or processes dynamically in or close to real-time. Their efficacy depends on data from various sources, such as sensors and simulations. Digital twins offer several advantages, including predictive maintenance, minimizing downtime, enhancing the lifespan of machinery, and optimizing production processes. This optimization promotes sustainability in manufacturing by curbing energy consumption, improving material efficiency, and reducing environmental waste. Despite these benefits, digital twin technology has not yet reached its full potential in manufacturing. Therefore, this study highlights how proactive maintenance and process optimization can enhance sustainability outcomes. It also emphasizes the importance of efficiently utilizing robust data analytics, data governance, and modelling techniques. To provide a thorough understanding of this ground-breaking technology, the study undertakes a systematic literature review on the associated implications of using digital twins. The review examines potential opportunities, challenges, and ethical considerations that can enhance the efficacy and sustainability of manufacturing processes. [ABSTRACT FROM AUTHOR]

The regulatory environment of a Major Defense Acquisition Program changes throughout its life cycle, challenging generations of leaders to be custodians of corporate knowledge, and make decisions across an enterprise, sometimes without a comprehensive view of factors influencing their programs. Tools such as Digital Twins, Digital Engineering, Model-Based Systems Engineering, and Modeling & Simulation have utility, but their value to managers is often illusive. This paper explores if program decision making can be digitally transformed by applying principles of decision science, theory & methods of systems engineering, and practices from business program management, to engineer decisions. This cumulative case study describes the background, purpose, method, and conclusions from four projects. A digital twin of a project can be constructed by modeling organization processes, digitalizing documents, linking live cross functional data, and connecting decisions to data to process. The resulting system has transparent processes, dynamic and relevant data models, and useful decision aids. This repository is an enduring, usable body of knowledge, linking decisions to the data required, and the business processes that create it. A program digital twin supports decision engineering: it identifies decision points, data required for those decisions, and processes necessary to produce the data. [ABSTRACT FROM AUTHOR]

Digital Twin is one of the many components in Industry 4.0. Unfortunately, the meaning of digital twin could be differed from one to another. It is very confusing when someone is asking for a "Digital Twin" solution, what does the person really want? IBM has defined digital twin as virtual representation of component or assembly of components that interact with one another limited for the purpose of product life management. Siemens defined it in three areas namely Product, Production and Performance twins. The name suggested limit the definition to its application and there is no relationship with another. Lastly Jones characterized the term to 19 areas which is too much for one to remember and to know how they are related to one another. The purpose of the paper is to re-classify digital twin from the perspective of its purpose of implementation. By knowing the need, we could recommend the level of digital twin to be adopted. [ABSTRACT FROM AUTHOR]

This study aims to establish an intelligent Cyber-Physical Production System (CPPS) with closed-loop control and self-optimization of process parameters. Empowered by a deep learning (DL) agent, the digital twin (DT) simulation can focus on the most significant process routes and eliminate 92.53% of unnecessary exhaustive iterations. On the other hand, DT can provide the most accurate simulation result for fast convergence to the optimal parameters of DL and no need to waiting for the result after the process completed. Though collaborative and autonomous interactions between DT and DL have been proposed recently, the practical integration of DT-DL and CPPS in the Electronic Manufacturing Service (EMS) industry has yet to be validated in official publications. With such novelty approach, the improvement of the production throughput (T/P) and rolled throughput yield (RTY) is 7.513% and 0.86%, respectively, which is on top of the contributions from traditional industrial engineering (IE) productivity tools. [ABSTRACT FROM AUTHOR]

A digital twin or digital replica is a virtual model of a physical asset such as a product, process, system, or facility. It utilizes data from an actual physical asset to understand better and augment its performance, powered by artificial intelligence (A.I.), machine learning, and data analytics. Digital twins can mirror a physical twin and reveal issues before they occur. They rely on sensors embedded in the physical world to transfer real-time data about the operative process and environment. The data collected from the connected sensors is then analyzed on the cloud and is accessible via a dashboard. Digital twins are powerful masterminds to drive innovation and performance. Unsynchronized production can easily cause problems such as the backlog of intermediate warehouses, unsmooth production, and long production cycles. Synchronized production helps to improve overall efficiency and reduce waste. The material handling, production logistics path, movement pattern, suspension, and caching mode of the WIP (Work-In-Process) need to be planned based on the equipment's action and behavior mode. Unloading, distributing, and delivering raw materials to the manufacturing unit and warehouse are all part of material handling. The digital twin technology provides a highly efficient runtime environment for simulating complex systems and searching for robust computational optimization models. Digital twin technology has a wide range of economic value depending on the monetization model. This study explored costly industrial or business equipment, services, or processes that can be optimized by reducing asset downtime and lowering overall maintenance costs. These capabilities are essential, making internal software competencies crucial to driving value. [ABSTRACT FROM AUTHOR]

Since its inception, the concept of a digital twin has been used in a broad range of fields, including product life cycle management, product development, manufacturing, logistics, and process control, to mention a few. As technology advances, physical objects and virtual worlds are being integrated and interconnected in a real-life context. However, the Supply Chain Digital Twin publishing is confined to a few topics. This study uses bibliometric analysis to graphically map scientific publications and research trends in the Digital Twin Supply Chain sector worldwide. The Scopus database was used to collect information for this study, and online analysis using the Scopus website and VOSViewer was used to demonstrate bibliometric network mapping. We use an article selection procedure starting with the searched keywords and year constraints and then exporting the database to RIS and CSV format files. We retrieved 179 scientific publications published between 2017 and 2021 from the Scopus database over the last five years. We also use VOSViewer to map the network. According to the database, researchers in Germany had the most published papers indexed by Scopus among the most prolific authors (N=30), with the United States coming second (N=27) and the United Kingdom third (N=16). This study recommends combining the research subjects of IoT for Sustainability in Agriculture: Digital Twin Concept, Impact, Disruption, Internet, abbreviated as DTCIDI research themes. [ABSTRACT FROM AUTHOR]

Late order loss for difficulty handling (loading and unloading) activities left an alarm message to make traditional transportation handling for distribution e-commerce more accessible through mitigating it to semi-auto actions. This article discusses the idea of Vehicle Containers made up of Permutational Drawers, i.e., VCPD, that ensure ergonomic handling. The proposed Ergonomic Digital Twin (EDT) manages the VCPD by the Internet of things, i.e., IoT. The VCPD object has two dimensions: drawers' size and motion mechanism. These targets are implemented via establishing the digital twins' model, i.e., DT, for these drawers to test its qualifying for implementation, mainly if supported by IoT. There is still much confusion regarding the DT and how it will apply to the VCPD in medium-sized schemes for transportation enterprises. This work activates the IoT to bolster and simplify transportation activities through designing VCPD and control via a unified framework having several standard steps to reduce execution time, effort, and transportation costs. [ABSTRACT FROM AUTHOR]

A Digital Twin is a virtual representation of a real dynamic system that can simulate its current conditions, predict its future behavior, and log valuable information about its internal operation and interactions with other systems. The unique capability of automatic bidirectional information flow between virtual and physical worlds and predictive analysis of Digital Twins are what make this emergent technology greatly innovative and of significant value. The objective of this case study is to implement a vehicle’s performance Digital Twin concept on an electric passenger bus and analyze the technology’s adaptability level, capabilities, scope, limits, and future improvements. This system incorporates a network of sensing devices mounted on the vehicle, a real-time simulation model, edge computing capabilities and the possibility of incorporating predictive maintenance models to determine remaining useful life of components. The Digital Twin is thought to have great value when it comes to gaining deep insight on the dynamic performance of the bus, analyzing energy waste, exploring determining factors of energy usage and monitoring the current and future state of the vehicle. All of this being possible through simulation technology, digital modeling, physical and virtual sensors, edge computing, Internet-of-Things networks, and Machine Learning algorithms. [ABSTRACT FROM AUTHOR]

A Digital Twin is a virtual representation of a real dynamic system that can simulate its current conditions, predict its future behavior, and log valuable information about its internal operation and interactions with other systems. A key feature is the capability of automatic bidirectional information flow between virtual and physical worlds. The objective of this work is to create a Living Lab for the demonstration of interacting urban Digital Twins under the United Nations’ Sustainable Development Goals of sustainable cities and communities, health and wellbeing, and industry, innovation, and infrastructure. By using a network of sensing devices mounted on a vehicle, the proposed system is capable of processing real-life data through edge computing, modeling software and Machine Learning algorithms. With the processed information, a 3D virtual representation of urban spaces and the vehicle itself, the interactions between both subjects and the evolution of each is enabled. This approach of Digital Twin technology for urban spaces has significant value when it comes to analyzing a community’s evolution, mobility, a vehicle’s dynamic behavior, and its interaction with urban infrastructure. This work presents the proposed methodology for developing Digital Twin concepts for urban spaces and vehicles as well as their respective characteristic components. [ABSTRACT FROM AUTHOR]

Modern manufacturing technologies such as Additive Manufacturing are a vital pillar of Industry 4.0 and Smart Manufacturing systems. One of the most promising AM techniques is Directed Energy Deposition (DED) which uses a thermal source to generate a melt pool on a substrate into which metal powder is injected. Laser Cladding (LC), a DED additive process, is widely used in machine parts repair and functional coating due to its advantages, such as the low heat input into the substrate. Compared to conventional welding, this results in less induced stress, lower dilution rate, small heat-affected zone, and good metallurgical bonding between the coating and the substrate. The LC technology is not as robust or standardized. The part quality, mechanical properties, and microstructure of Laser- Cladded parts are neither as commercially predictable nor controllable. This research aims to develop Deep Learning models that predict and classify the LC process for LC coating and single and interfering-bead samples. The ML models will detect faulty process input parameters and predict the quality. This detection and prediction take place in real time, which is vital in producing the additive process's data model component of a Digital Twin (DT). Digital imaging of a coaxial CMOS camera and melted pool temperature measurements using a LASCON LPC04 pyrometer are being used as in-situ sensory devices. A plethora of articles have limited their ML models to individual combinations of substrate and powder and focused on the three main process parameters (laser power, laser beam scanning speed, and Powder feeding rate). This research project expands the scope to include a different substrate and powder combinations, utilize additional parameters, and exploit different powder/substrate combinations. The Inputs will be gathered and manipulated in a controlled environment to predict both good-quality and poor-quality LC. The process will take place by building geometries selected strategically to associate uncontrollable parameters, especially the temperature of the substrate, in the prediction model. The online optical images and the offline microscopic analysis of the sectioned bead are mapped together. In a later stage, the ML can potentially integrate into a control system and digital twin models. [ABSTRACT FROM AUTHOR]