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Digital Twin technology is the concept surrounding the creation of a digital "twin" or replica of a physical asset. Digital twins are interactive, artificial intelligence (AI)-based virtual models that serve as digital replicas of real-life objects and/or environments. The benefits of a Digital Twin is to provide an abstraction layer that allows for applications to interact with a device or devices in a consistent manner. The vision of a digital twin is that it follows the lifecycle of a device and the data associated with the device. Digital Twins will enable features like device simulation during development, integration of analytics and Machine Learning (ML) during deployment, and so on. The Reality of Digital Twins for IoT | Ian Skerrett - Medium
The Digital Twin can allow companies to have a complete digital footprint of their products from design and development through the end of the product life cycle. This, in turn, may enable them to understand not only the product as designed but also the system that built the product and how the product is used in the field. With the creation of the digital twin, companies may realize significant value in the areas of speed to market with a new product, improved operations, reduced defects, and emerging new business models to drive revenue. The Digital Twin may enable companies to solve physical issues faster by detecting them sooner, predict outcomes to a much higher degree of accuracy, design and build better products, and, ultimately, better serve their customers. With this type of smart architecture design, companies may realize value and benefits interactively and faster than ever before. Industry 4.0 and the digital twin | Deloitte Insights
Digital twins offer a range of benefits across various industries, revolutionizing operations and driving efficiency. Here are some key advantages of using digital twins in industry:
- Enhanced Monitoring: Digital twins provide real-time insights into the behavior of physical products or processes, enabling companies to track performance trends, health, and potential failures proactively.
- Cost Reduction: Organizations can reduce costs and minimize potential failure rates through the use of digital twins, leading to more efficient resource utilization and maintenance practices.
- Improved Decision Making: By offering a comprehensive view of asset performance, digital twins empower organizations to make more informed decisions, ultimately leading to improved outcomes.
- Streamlined Design Process: Digital twins facilitate faster product innovation by allowing companies to simulate and test new products before investing in physical prototypes, thus accelerating the design process.
- Predictive Maintenance: With IoT sensors generating real-time data, businesses can proactively analyze information to identify and address system issues before they lead to breakdowns, enhancing production line efficiency and reducing maintenance costs
- Real-time Remote Monitoring: Digital twins enable remote access to monitor and control system performance from anywhere, providing a detailed view of large physical systems that would otherwise be challenging to obtain in real-time.
- Improved Team Collaboration: Automation processes and continuous access to system information enhance team collaboration among technicians, boosting productivity and operational efficiency.
- Data-backed Decision Making: By integrating financial data into virtual representations, businesses can make better financial decisions faster, leveraging real-time data and advanced analytics for improved outcomes.
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Watch NVIDIA Introduce Latest Metaverse Vision (FULL REVEAL)
At NVIDIA's CEO, Jensen Huang, talks about its Omniverse platform and new technologies to power its metaverse.
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Visualizing Data in Digital Twins with Unreal Engine | Webinar
Digital twins are 3D models of physical entities with live, continuous data updating functions and processes in real time. These models provide means for analyzing and optimizing structures. In this webinar, we demonstrate how Unreal Engine can be used to visualize this connected data. Ludwig Lovén from WSP introduces the Microsoft Azure Digital Twin (ADT) Link for Unreal Engine plugin, and provides an overview of the accompanying sample project. He then dives further into the plugin to explain how AI and particle systems can be used for compelling visual feedback.
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Learn About Digital Twin Technology Visualization with SAP and LeverX (Webinar)
Learn how digital twins – and networks of digital twins – are transforming how engineers design products, how manufacturers collaborate with end users, and how companies generate revenue and provide value. In this webinar, gain specific insights to the below: 1. Learn best practices for combining 3D and 2D data to your digital business data 2. Automate and integrate visual processes, including supplier collaboration, kitting and warehouse operations, manufacturing, assembly and test, training, field service and support; to ensure they all run smoothly with minimal human intervention and digital twin alignment. 3. Consolidate end-to-end asset or product lifecycle data into digital threads 4. Drive innovation in manufacturing, R&D, supply chain, asset management, service, and logistics
https://leverx.com/
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Deep Dive: Azure Digital Twins Updated Capabilities
Learn more about Azure Digital Twins Updated Capabilities: Come learn about the recently launched new version of the service, including an overview of the new capabilities, deep dive on architecture and technical walkthroughs. In this session, we will do technical walkthrough of the following: - Services - SDK & CLI - Modeling - Twin Graph - Graph Queries - Access Control Azure Digital Twins is an IoT platform that enables enterprises to create a digital representation of any assets, environments and business systems. Deep Dives are interactive training live events for developers, architects, or anyone building IoT solutions. Microsoft engineers & guest speakers do technical deep dives about a new feature or scenario. Come to learn more and ask questions in the live Q&A! Guest Speakers: Christian Schormann - Azure IoT Digital Twins Deep Dive Host: Pamela Cortez - Azure IoT Senior PM
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Virtual IoT | Digital Twins go open source: Eclipse Ditto introduction
Slides IoT Developer Survey: https://www.surveymonkey.de/r/eclipse...
Digital Twins are getting more and more established as IoT service providers (alternatively calling them device shadows or device twins) and industrial IoT players adapt and promote the concept. This introduction has not the goal to fantasize about possible applications of the buzzword "Digital Twin" but to put flesh to the bones. Eclipse Ditto provides aspects of the Digital Twin pattern which from our point of view is used for abstracting from physical devices: turning devices into APIs with the help of their data * handling access control: who or what is allowed to interact in which ways? * dealing with Twin and live state * routing messages to/from devices * enhancing Digital Twins by integrating foreign systems - e.g. "gather all the spare parts for this Twin"
- being able to search and select Twins via their metadata + reported state
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How to Build a True Digital Twin with Self-Configuring Models Using the Material Handling Library
This advanced webinar demonstrates how you can create a self-configuring model using the Material Handling Library. 👉 Description and links below... Learn to create a model where all components are created directly from simple input data. On startup, the model loads all elements (conveyors, transporters, machines…) from the provided data, connects all the elements and lets products be manufactured. This is achieved with little coding, and using standard AnyLogic capabilities such as agent populations and flow-chart blocks. With such a setup, you can create true digital twins - as your model will always reflect your current data reality, without further manual refinements. Timestamps: context intro (Jeff Bezos calls) About Benjamin Schumann Jeff calls again (Why use self-configuring models) Webinar task overview and Skill Warning Chapter 1 – A simple example of self-configuring model creation Chapter 2 – More advanced creation of conveyors Chapter 3 – Auto creation of AGV transportation fleet Next steps Webinar Host
Benjamin Schumann, Ph.D is a simulation expert with over 10 years of experience applying simulation and AnyLogic. Ben leads his own simulation practice advising global consulting firms as well as industrial and academic clients. Previously, he championed the potential of simulation as a consulting tool with McKinsey & Company, Inc. as well as working for boutique consulting firms as a modeller. Ben holds a PhD in Complex Systems Simulation for Aerospace Design. Ben's homepage - https://www.benjamin-schumann.com/
Webinar model with download files
Material design UI library from Goldratt Research Labs
AnyLogic Material Handling Library
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Double Vision: Using ‘Digital Twins’ To Pair Virtual And Physical Worlds
Being able to “see it before you build it” has long been a dream of manufacturers, but only recently has technology advanced to the point of making this a reality. Enter “digital twins”—virtual representations of products created with 3D design software, and another technology on Goldman Sachs Research’s “Outsiders” list of emerging ecosystems to watch. By building virtual models to test in reconstructed “real world” operating environments, companies can get an accurate picture of how their products will behave in the field, explains Goldman Sachs Research’s Joe Ritchie, ultimately enabling better performance through predictive maintenance and enhanced design. The potential goes beyond capital goods; as digital twins evolve, the data they gather could be combined with consumer information to create more targeted go-to-market strategies for industries like insurance and advertising. Learn more: https://link.gs.com/WSCa
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Minds + Machines: Meet A Digital Twin
GE Digital Colin J. Parris, Ph.D., VP of Software Research, GE Global Research Center, offers a fascinating look at Digital Twin technology. Learn how this convergence of the mind of humans talking to the mind of machines is profoundly changing everything we know. A living model driving business outcomes, Digital Twin brings see-think-do capabilities to industry, informing and executing at the edge. LEARN MORE ABOUT GE DIGITAL: https://www.ge.com/digital
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The Rise of Transformer AI and Digital Twins in Healthcare
Transformer AI models are powering a new era of life sciences, helping researchers encode the structure and function of biology and chemistry, making sense of unstructured patient data, and improving detection and diagnosis in #medicalimaging. At the same time, advances in digital twin technology are powering researchers and clinical teams to simulate cells, organs, and surgeries to better understand workflows and improve patient outcomes.
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AI and Digital Twins Powering Plant Efficiency and Reliability by ARC and TCS
TCSGlobal
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Digital twin: Making your asset smarter with the digital twin
Central to our next-generation offering, and supporting an ecosystem of asset-centric engineering applications, is the concept of a cloud-based digital twin. The digital twin is a virtual image of your asset, maintained throughout the lifecycle and easily accessible at any time. One platform brings all the experts together, providing powerful analysis, insight and diagnostics. Learn more at www.dnvgl.com/digitaltwin
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Port of Rotterdam: Europe's Strongest Digital Twin Case Study | IoT World Europe 2018 webinar
IoT World Series The Port of Rotterdam talk us through their digital twin implementation. Put industrial IoT into action at IoT World Europe → https://goo.gl/Q3muQi #IoTWorldEurope
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Digital twins powered by IIoT and Industrial AI - Diego Galar, Luleå University of Technology
Digital twins powered by IIoT and Industrial AI: A cocreation between data scientists and engineers - Diego Galar, Professor at Luleå University of Technology. Data Octagon - The Future of Data - Live-Streamed Stage from the Data Innovation Summit 2020 Data Octagon is a live-streaming stage that will accommodate some of the biggest names in the world of DATA, part of the annual Data Innovation Summit. Built as an independent program consisting of 30 to 45-minute panels and 20-minute Tech News, this technology-oriented program will present insight into current data policies, trends, challenges and opportunities, as well give overview of the latest technological breakthroughs, and glimpse into future of Data Management and insight. The program was hosted by Robert Luciani, the CEO of Foxrane, and live-streamed during the 2020 edition of the annual Data Innovation Summit on Twitter (https://twitter.com/DISummit2030) and YouTube (Hyperight AB).
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Product Lifecycle Management - Leveraging AI and digital twins to transform the industrial system
Digital twin technology is revolutionizing the way industrial companies approach manufacturing operations. Digital twins unite physical entities with virtually modeled entities based on technologies. We present our experience with different digital twin technology. How we connect Model-based simulation with shop floor connectivity and how to use digital twin to optimize AI application. Join this webinar to find out more!
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IAn Introduction To Intelligent Digital Twins
During this informative session, XMPro CEO, Pieter Van Schalkwyk will delve into the concept of Intelligent Digital Twins and discuss how they differ from traditional Digital Twins. This webinar aims to equip professionals across various domains with the knowledge necessary to harness the power of AI, accelerating the adoption and implementation of IDTs in diverse industries.
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Digital twin technology’s role in industry innovation: Digital Twin Summit 2017
Most companies now see how important digital twin technology and the digital thread are for their future business. Now they want to know how to execute it. Learn more from Chris Stevens, Vice President of Automotive and Transportation at Siemens PLM Software, and Barry Chapman, Vice President of Aerospace and Defense Siemens PLM Software.
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Siemens PLM - The Real Value of the Digital Twin
Challenge conventions in design and manufacturing with a Digital Twin of your product, manufacturing process and production system.
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The Digital Twin: Realizing Transformation (Introduction)
At Siemens PLM Connections (May 2017), President and CEO Tony Hemmelgarn discusses the importance of digitalization as an agent for business transformation, and explains how leveraging a digital twin for product development, production and market performance is critical to realizing major business improvements and disruptive innovations.
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Nuclear Plasma Control
The accident at Three Mile Island (TMI), which occurred on March 28, 1979, was a partial meltdown of the nuclear reactor core that resulted from a series of equipment failures and human errors. During the accident, the control room operators received a series of confusing and conflicting signals from the reactor's instruments, which made it difficult for them to understand what was happening and respond effectively. The control room board did not suddenly light up in seconds, but rather displayed a complex and evolving pattern of alarms and indications over a period of several hours. The initial alarm that alerted operators to the problem was a high-pressure indication in the reactor coolant system. Over the next several hours, operators received a series of alarms related to coolant flow, water levels, and reactor temperature, among other factors. The complexity and ambiguity of these signals contributed to delays and errors in the operators' response, ultimately leading to the partial meltdown of the reactor core. The accident at Three Mile Island highlighted the need for improved training, communication, and safety protocols in the nuclear power industry. It also led to significant changes in the regulation and oversight of nuclear power plants, as well as increased public scrutiny and concern about the safety of nuclear energy.
Researchers have successfully demonstrated the world's first predictive control of a fusion plasma using a digital twin approach. The key points are:
- Researchers from Kyoto University, the National Institute for Fusion Science, and the Institute of Statistical Mathematics in Japan conducted an experiment to control the electron temperature of a fusion plasma using electron cyclotron resonance heating (ECH), while optimizing the predictive model based on real-time observations of the electron density and temperature profiles.
- This "data assimilation-based control" approach allowed them to bring the electron temperature close to the target while improving the prediction accuracy of the model. This was the first successful demonstration of predictive control of a fusion plasma using a digital twin.
- The digital twin is a virtual replica of the actual fusion plasma that receives real-time data from the experiment. It uses machine learning to anticipate the performance of the physical plasma, allowing researchers to test control strategies without making irreversible changes to the real system.
- This control approach is expected to be fundamental for the advanced control systems needed to realize fusion power generation, such as plasma profile control and avoidance of instabilities. It could also be applied to other complex systems with many uncertain factors, like traffic and water management.
- The research team believes this is an important step towards the advanced control capabilities required for practical fusion power plants.
In summary, the search results describe the first successful demonstration of predictive control of a fusion plasma using a digital twin approach, which is a significant milestone towards the realization of practical fusion energy
Computerized Maintenance Management (CMM)
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AI & IoT Predictive Maintenance | Symphony | #CoCoonpitch
#CoCoonPitch enables entrepreneurs to present their products or services to potential investors, co-founders, teammates, customers or corporate partners. Symphony - Alain Garner
86% of maintenance is scheduled either too late or unnecessarily and we want to change that. Using our Symphony sensors we can predict the unpredictable & tell when machines will fail using our industry 4.0 wireless sensors that generate plug & play, predictive maintenance. If you want to pitch, send your deck to pitch@hkcocoon.org
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Customer Journey
Gartner defines digital twin of a customer (DToC) as a dynamic virtual mirror representation of a customer that simulates and learns to emulate and anticipate behavior. ... DToC represents a nascent technology that can revolutionize demand forecasting accuracy, vastly improve customer experience and serve as a critical input to enhance the use of AI and machine learning (ML) tools. While 52% of survey respondents viewed AI as an “important and disruptive” technology and 40% gave the same description for digital supply chain twins, only 27% specifically described DToC as an “important and disruptive” technology. Gartner: Full value of supply chain ‘digital twins’ goes unrecognized | Dan Berthiaume - Chain Store Age ... 60% are piloting or plan to implement a digital supply chain twin (DSCT), just 27% were also planning to incorporate a digital twin of a customer (DToC) as part of their digital strategy.
