Digital Twin in Industry 4.0 | Applications and its Challenges

Introduction to Digital Twin in Manufacturing

With the application of the advanced generation of IT, a new industrial revolution is in full fluctuation. the important direction of an industrial revolution and many countries have launched their manufacturing development strategies. Among these new strategies, smart manufacturing has become an essential direction of industrial development and the industrial revolution. Smart manufacturing has been valued by all countries worldwide.

A digital twin is an automated representation of a physical device, object, or service. It can be an automated copy of an object in the real world, such as wind farms, jet engines, or other larger items, such as whole cities or buildings. The digital twin technology can be used to duplicate processes to gather data so that one can predict how it will perform in the future.

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What is Smart Manufacturing?

Smart Manufacturing (SM) is a technology-driven approach to monitoring production processes using machines connected to the Internet. SM's goal is to automate operations and identify opportunities to improve manufacturing performance using data analysis.
SM is a specific application of the Industrial Internet of Things (IIoT). Deployment involves embedding sensors in manufacturing machines to collect operational status and performance data. Previously, this information was stored in a local database for individual devices and was only used to identify the root cause after a device failure.
By analyzing data flowing from machines in the entire factory or multiple factories, manufacturing engineers and data analysts can look for signs that certain parts may be out of order or unplanned by predictive maintenance. One can avoid equipment downtime.
For example, the SM system can automatically order more raw materials than in stock, assign other equipment to manufacturing orders as needed to complete the order, and prepare the distribution network once the order is completed.

What is a Digital Twin?

Digital twins are the ability to create a virtual representation of the physical elements and dynamics of how the Internet of Things(IoT) device works. It's more than a blueprint, more than a plan. It's not just an image. It's more than "virtual reality" glasses. It is a virtual representation of the elements and dynamics of how an IoT device responds throughout its life cycle. There are many things, such as jet engines, buildings, factory floor processes, etc.

Digital twins are essentially computer programs that use real-world data to create simulations that can predict the performance of a product or process. These programs can integrate IoT, AI, and software analytics to improve performance.

Advances in machine learning and big data have made these virtual models an integral part of the latest technology to drive innovation and improve performance. In short, creating one enables one to improve strategic technology trends, prevents costly failures of physical objects, and uses predictive capabilities, services, advanced analytics, testing procedures, and monitoring.

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How does Digital Twin Technology work in manufacturing?

The digital twin technology begins with applied data science researching the physics or mathematics and operational data of a system or physical object to create a mathematical model which will simulate the original.

Developers creating digital twins allow virtual computer models to receive feedback from sensors that collect data from the actual version. This allows the digital version to mimic and simulate what is happening in the original version in real-time, allowing insights into performance and potential issues.

Digital twins can be as complex or simple as needed, with varying amounts of data determining how closely the model simulates the actual physical version. One can also use the twin with a prototype to provide feedback on the product under development or act as a standalone prototype to model what happens to the physical version during build.

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What are the challenges of Digital Twin?

The digital twin can be used in various industries, from power generation and automotive to the alternative. It has been used to solve various problems. These challenges include exhaust testing and corrosion resistance of offshore wind turbines and improving the efficiency of racing cars. Other applications include hospital modeling to determine workflows and staffing to find process improvements.

Digital twins allow users to explore product process improvement, product lifecycle extension, product development, and prototype testing solutions. In these cases, the digital twin can effectively represent a problem so that the solution can be developed and tested programmatically rather than in the real world.

What are the applications of Digital Twin?

Digital twins are getting used in various industries for many purposes and applications. Some of the examples are listed here:

Smart cities - Digital twins are getting used to helping cities become more environmentally, socially, and economically sustainable. Virtual models can guide planning decisions and provide solutions to many of the typical challenges modern cities face.
Healthcare - The medical department is benefiting from digital twins in areas such as surgery training, organ donation, and risk reduction during surgery. The system also models the flow of people through the hospital and tracks where the infection is and who may be at risk of contact.
Manufacture - Digital twins can make construction more streamlined and productive while lowering the throughput time.
Retail - Outside of industry and manufacturing, digital twins are used in retail to model and enhance the customer experience.
Disaster Management - Global climate change has affected the world in recent years, so digital twins can help counter this problem by creating more intelligent infrastructure, climate change monitoring, and emergency response plans.

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What are the benefits of using Digital Twin?

The benefits of using Digital twins are:

Real-time remote monitoring - To get a detailed overview of an extensive physical system in real-time is often very difficult or even impossible. However, the digital twins can be accessed from anywhere, allowing users to monitor and control the system performance remotely.
Accelerated risk assessment and production time - Digital twins allow companies to validate and test their products before they exist in the real world. By replicating the planned production process, the digital twin allows engineers to identify process errors before the product goes into production.
Engineers can disrupt the system to examine the system's reaction, synthesize unexpected scenarios, and identify corresponding mitigation strategies. This feature improves risk assessment, accelerates new product development, and increases production line reliability.
Better team collaboration - 24x7 access and process automation to system information allow technicians to focus more on collaboration between teams and operational efficiency.
Predictive maintenance - The IoT sensors in a digital twin system generate large amounts of data in real-time, allowing enterprises to analyze the data to identify problems in the system proactively. This feature allows enterprises to plan predictive maintenance more accurately, improve production line efficiency, and reduce maintenance costs.

Conclusion

Digital twins help companies to create value, generate new revenue streams, and answer key strategic questions. With new technology features, agility, capabilities, lower cost, and flexibility, organizations embark on a journey to create digital twins with less capital investment and time value than ever before. The future of digital twins is almost endless, as more and more cognitive power is being used. As a result, digital twins are constantly learning new skills and abilities, which means that one can continue to generate the insights needed to make processes more efficient and produce better products in the future.