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What the Integration of Cognitive Twin Tech and IoT Will Mean for Consumers and Industry

The integration of cognitive digital twin technology with the Internet of Things (IoT) has the potential to revolutionize the marketplace by providing companies with valuable insights into their products and processes.

What is Cognitive Digital Twin Technology?

Cognitive digital twin technology is a virtual model of a physical system that uses data and artificial intelligence (AI) to simulate and predict the behavior of that system. This technology combines data from sensors and other sources with machine learning algorithms to create a digital representation of a physical system.

A cognitive digital twin model can be used to monitor and analyze the behavior of a system in real-time, and it can be used to simulate the behavior of that system under different conditions. By using this technology, companies can gain insights into the performance of their products, optimize their operations, and reduce maintenance costs.

What is the Internet of Things (IoT)?

The Internet of Things (IoT) is a network of physical devices, vehicles, home appliances, and other items that are embedded with sensors, software, and other technologies that enable them to connect and exchange data with other devices and systems over the Internet.

IoT devices can collect data from their environment, such as temperature, humidity, and pressure, and transmit that data to other devices or systems for analysis. By using IoT devices, companies can monitor their products and processes in real-time and gain insights into how they are performing.

The Impact of Integrating Cognitive Digital Twin Technology With IoT?

Cognitive digital twin technology can be integrated with IoT by using data from IoT devices to create a digital twin model of a physical system. IoT devices can provide data about the performance of a product or process, which can be used to create a digital twin model.

The digital twin model can then be used to simulate the behavior of the physical system under different conditions and to predict how the system will behave in the future. By using IoT data to create a digital twin model, companies can gain insights into the performance of their products and processes, and they can optimize their operations to reduce costs and improve efficiency.

There are several benefits to integrating cognitive digital twin technology with IoT, including:

  1. Predictive Maintenance: By using a cognitive digital twin model, companies can predict when maintenance is required on their products or processes, reducing downtime and maintenance costs.
  2. Improved Efficiency: By monitoring the performance of their products and processes in real-time, companies can optimize their operations to improve efficiency and reduce costs.
  3. Reduced Waste: With CDT, companies can reduce waste by identifying areas where resources are being wasted.
  4. Enhanced Product Design: By using a cognitive digital twin model, companies can simulate the behavior of their products under different conditions and make design changes in the earlier stages of R&D to improve performance, reduce costs, and cut time from POC to market.
  5. Improved Customer Experience: By monitoring the performance of their products in real-time, companies can improve the customer experience by identifying and addressing issues before they become major problems.

How the Market is Already Benefiting from Digital Twin and IoT Technologies

Many industries are already benefiting from the kinds of integration between CDT and IoT technologies. Chief among these is the transportation industry.

Cognitive digital twin technologies coupled with IoT are already proving invaluable for predictive maintenance of high-value military vehicles, airplanes, ships, and even passenger cars. For example, digital twin solutions like those developed by CarTwin extend the lifespan of cars and other vehicles by monitoring the vehicle’s “health” through its “digital twin.”

Basically, CarTwin can provide diagnostic and predictive models for all vehicle systems for which data is available (either directly or indirectly) onboard the vehicle.

Virtually any part of the vehicle that has sensors or that sensors can be developed for can be “twinned.” These data sets are then enhanced and augmented with design and manufacturing data that is already available by the OEM.

Primarily designed for use in fleets of vehicles, in combination with powerful AI models, CarTwin predicts breakdowns, monitors and improves performance, and measures and records real-time greenhouse gas emissions, which reduces expensive maintenance costs and avoids lost revenue associated with fleet downtime.

You can read much more about how AI and digital twin technology in my new book Quantum Care: A Deep Dive into AI for Health Delivery and Research. While the book’s primary focus is on healthcare delivery, it also takes a deep dive into digital twin tech, with an entire chapter devoted to CDT, as well as IoT, and the development and launch of CarTwin!

Rohit Mahajan is a Managing Partner at BigRio and the President and Co-Founder of Citadel Discovery. He has a particular expertise in the development and design of innovative AI and machine learning solutions for clients in Healthcare, Financial Services, Retail, Automotive, Manufacturing, and other industry segments.

CarTwin has leveraged AI and Digital Twin technologies to create a digital, cloud-based clone of a physical vehicle designed to detect, prevent, predict, and optimize through AI and real-time analytics. If you would like to benefit from our expertise in these areas or if you have further questions on the content of this article, please do not hesitate to contact us.

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Digital Twin Technology Being Used to Create Next-Gen Aircraft

Digital twin technology is becoming more and more mainstream, particularly when it comes to the maintenance and monitoring of complex mechanical systems such as aircraft.

GENEX, an EU-funded startup, has announced the creation of a new digital twin framework that will be used to create “the next generation” of safer and greener composite aircraft.

Launched on Sept. 1, 2022, GENEX is a 42-month, 5.7 million Horizon Europe collaborative research project seeking to develop an end-to-end digital twin-driven framework for optimized manufacturing and maintenance of next-generation composite aircraft structures.

According to the company, “enhanced computational models will embed interdisciplinary knowledge of aircraft components and manufacturing/repair processes to support their optimization as well as enable data management and monitoring across the entire life cycle of the aircraft’s operation.”

GENEX will really ramp up in the New Year. It is projected to run through 2026.

According to a company press release about the initiative, “The core of the GENEX project is based on three [digital twin] blocks focused on different facets of the aircraft use life, which, eventually, will be integrated into a fourth to form the multidisciplinary digital twin.”

Each block is as follows:

     • Block 1 – Manufacturing Process Digital Twin: An automated tape laying (ATL) process, coupled with hybrid-twin simulation methods, will be developed for the eco-efficient and advance manufacture of recyclable thermoplastic composites.

      • Block 2 – Product Usage Digital Twin: Data- and physics-based machine learning (ML) algorithms for damage detection and location, combined with high-performance computing (HPC)-based multi-physics and AI-powered digital twin tools for fatigue life prediction.

      • Block 3 – MRO Digital Twin: Augmented reality tools, together with novel laser-assisted methods for surface cleaning and monitoring, smart monitoring, and in-situ tailored heating of composite repair blankets, will be further developed to provide additional assistance in manual scarf repair operations, increasing the reliability of the repair process, while supporting the modification and virtual certification of MRO practices.

    • Block 4 – Cognitive Digital Twin: Combined integration of blocks 1-3 for the realization of a digital twin-drive framework implemented into a common industrial internet of things (IIoT) platform.

“The aviation industry is facing a two-fold challenge — targeting carbon neutrality while also adopting the digitalization of next-generation aircraft,” Dr. Calvo-Echenique says. “In GENEX, we hope to provide the needed technological impulse to optimize composite components manufacturing, and operation and repair processes using digital twin strategies.”

Other Industries Benefiting from Digital Twin Technologies

As you can see from the GENEX project, AI and digital twinning are revolutionizing many industries, chief among them transportation. Cognitive digital twin technologies are proving invaluable for the predictive maintenance of high-value military vehicles, airplanes, ships, and even passenger cars. Digital twin solutions like those developed by CarTwin extend the lifespan of cars and other vehicles by monitoring the vehicle’s “health” through its “digital twin.”

Basically, CarTwin can provide diagnostic and predictive models for all vehicle systems for which data is available (either directly or indirectly) onboard the vehicle.
Virtually any part of the vehicle that has sensors or that sensors can be developed for can be “twinned.” These data sets are then enhanced and augmented with design and manufacturing data that is already available by the OEM.

Primarily designed for use in fleets of vehicles, in combination with powerful AI models, CarTwin predicts breakdowns, monitors and improves performance, measures and records real-time greenhouse gas emissions, which reduces expensive maintenance costs and avoids lost revenue associated with fleet downtime.

Rohit Mahajan is a Managing Partner at BigRio and the President and Co-Founder of Citadel Discovery. He has a particular expertise in the development and design of innovative AI and machine learning solutions for clients in Healthcare, Financial Services, Retail, Automotive, Manufacturing, and other industry segments.

CarTwin has leveraged AI and Digital Twin technologies to create a digital, cloud-based clone of a physical vehicle designed to detect, prevent, predict, and optimize through AI and real-time analytics. If you would like to benefit from our expertise in these areas or if you have further questions on the content of this article, please do not hesitate to contact us.

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NOAA is Creating a Digital Twin of the Planet Earth!

A digital twin is a digital representation of a physical object or system. Cognitive digital twin (CDT) technology uses AI to create highly sophisticated “twins” or models to accurately mimic a real-world object. That object could be a car, an airplane, ship, or helicopter. Increasingly in the healthcare field, CDT has been used to “twin” organs and biological systems for research, diagnostics, and health maintenance. Digital twins have even been built to represent and understand regions and cities.

Now, in perhaps one of the most complex and ambitious uses of CDT to date, the National Oceanographic and Atmospheric Association (NOAA) has announced its plans to create a digital twin of the planet Earth to track global warming and other environmental issues!

The agency has partnered with NVIDIA and Lockheed Martin to construct the Earth Observation Digital Twin, an inaugural prototype of Earth modeled on real-time geophysical data sourced from satellites and ground stations.

According to NOAA, the replica Earth, or EODT, will be designed as a two-dimensional computer program. Some potential climate impacts the EODT can display include global glacier melting, drought impacts, wildfire prediction, and other climate change events.

“We’re providing a one-stop shop for researchers, and for next-generation systems, not only for current, but for recent past environmental data,” Lockheed Martin Space Senior Research Scientist Lynn Montgomery said. “Our collaboration with NVIDIA will provide NOAA a timely, global visualization of their massive datasets.”

Emerging technologies like artificial intelligence play a key role in EODT’s data processing and modeling. Matt Ross, a senior manager at Lockheed Martin, said that the sheer volume and diversity of data from NOAA sources that program EODT make it challenging to gauge accurate insights from the application without the use of AI.

“This data happens to come in different formats, because the data are so diverse, because it’s measuring so much different stuff,” Ross said. “It arrives in different formats that, absent technology, it could make it very, very difficult to gain the insights that NOAA needs to make decisions.”

Leveraging the power of AI and machine learning algorithms will help NOAA researchers assimilate and identify the incoming data, as well as detect any anomalies. Ross added that the combined power of AI and ML data processing is key to Lockheed and NVIDIA’s “digital twin” programming technology in that it can accurately model past data as well as future realities, all in an intractable and real-time interface.

While both NVIDIA and Lockheed intend for the final deliverable to be a two-dimensional user experience, additional capabilities may be added in the future.

“The fact that we can pull all this data into a single sort of format, in a single viewpoint, allows you to have real-time or near real time, access to it, and the interdependencies of that data to make real-time decisions,” said Dion Harris, the lead product manager of accelerated computing at NVIDIA.

Other Industries Benefiting from Digital Twin Technologies

In addition to paving the way for a digital twin of the planet itself to monitor climate change, AI and digital twinning are revolutionizing many other industries, chief among them transportation. Just as CDT can monitor the health of the Earth, cognitive digital twin technologies are proving invaluable for predictive maintenance of high-value military vehicles, airplanes, ships, and even passenger cars. Digital twin solutions like those developed by CarTwin extend the lifespan of cars and other vehicles by monitoring the vehicle’s “health” through its “digital twin.”

Basically, CarTwin can provide diagnostic and predictive models for all vehicle systems for which data is available (either directly or indirectly) onboard the vehicle.

Virtually any part of the vehicle that has sensors or that sensors can be developed for can be “twinned.” These data sets are then enhanced and augmented with design and manufacturing data that is already available by the OEM.

Primarily designed for use in fleets of vehicles, in combination with powerful AI models, CarTwin predicts breakdowns, monitors and improves performance, measures and records real-time greenhouse gas emissions, which reduces expensive maintenance costs and avoids lost revenue associated with fleet downtime.

Rohit Mahajan is a Managing Partner at BigRio and the President and Co-Founder of Citadel Discovery. He has a particular expertise in the development and design of innovative AI and machine learning solutions for clients in Healthcare, Financial Services, Retail, Automotive, Manufacturing, and other industry segments.

CarTwin has leveraged AI and Digital Twin technologies to create a digital, cloud-based clone of a physical vehicle designed to detect, prevent, predict, and optimize through AI and real-time analytics. If you would like to benefit from our expertise in these areas or if you have further questions on the content of this article, please do not hesitate to contact us.

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