With significant advancements in machine learning technology and big data, virtual models have become a staple in modern engineering to drive innovation and enhance performance. Optimizing IoT, Artificial Intelligence, and data analytics, the virtual replicas of physical devices are created by data scientists and IT personnel before building and deploying actual devices to help predict their future with advanced analytical, monitoring, and predictive capabilities, test processes and services. The digital representation of physical objects is popularly known as Digital Twins as it takes the real-data world as inputs for making output predications or simulations of how the physical object or system would work or get affects based on those inputs. In a way, digital twin offers engineers a glimpse of what is happening or what can happen, with physical assets to meet the new realities of software-driven products fuelled by digital disruption. In simple language, digital twin refers to a highly complex virtual model that is the exact counterpart of a physical thing, which could be a car, tunnel, bridge or even a jet engine. Thanks to rapidly evolving simulation, better interoperability and IoT sensors, enhanced computing infrastructure, and more availability of tools, which is helping digital twins trend to gain a momentum.
How does digital twin integrate with the physical product?
The developers of digital twins determine the physics that underlie the physical product or system and utilize that data to develop virtual copy of the assets. The digital twin receives input from sensors gathering data from the physical product/system to simulate the physical object in real time, offering insights into performance and potential problems. The digital twin can be constructed based on a prototype of its real-world counterpart before any physical version is built. The digital twin can be simple or complicated based on the amount of data used up for building and updating it and the type of physical object. Digital twin technology incorporates the intricate process of virtual performance of elements and dynamics of the connected devices throughout its lifecycle. Demonstrating the ability to integrate the components into a single unit of operational-oriented design, digital twins ensure the superb quality of any product.
Digital twin applications
Product Prototyping
Digital twin capabilities can streamline the design process and eliminate many aspects of prototype testing using 3D simulations and human-computer interfaces such as augmented and virtual reality. The digital tool helps engineers identify potential manufacturability, quality, and durable issues even before finalizing the designs, thus it accelerates traditional prototyping, moving products into production more efficiently and at a lower cost. The digital twin can also help to gain a wealth of insights from the way customers are using a product and use that data to identify potential faults, troubleshoot from afar and eliminate unwanted functionality or components from the product.
Predictive Maintenance
The digital twins are created to transform the way companies perform predictive maintenance of products, identifying and addressing malfunctions even before they happen. Sensors embedded in the machines feed performance data into the digital twin in real time, which helps to tailor service and maintenance plans for effective asset management, enhanced worker safety, reduced risk of accidents, lower maintenance costs, and improved customer satisfaction. The best part is digital twins provide engineers a detailed and intricate view of a physical asset without needing for the engineer and asset to be in the same room, or even country. The digital twins enable smart industrial applications for real world operational developments.
Patient Care
The use of digital twin is not limited to aerospace, heavy machinery, or inanimate objects. The digital twin is now being incorporated in the healthcare industry for monitoring and managing a patient’s vital data using a biophysical model. Integrating sensor data to cloud-driven analytics, the healthcare providers can offer the effective treatment in managing the patient’s health remotely. The virtual representation of the patient’s organ allows the surgeon and healthcare professionals to analyse medical problems and practice procedures in a simulated environment.
Operational Efficiency
Leveraging digital twins, the industries can optimize supply chains, distribution, and fulfilment operations by creating virtual models of factories at different locations. The IoT sensors embedded in factory machines feed performance data into AI and machine learning applications for analysis, which is then fed into the digital twin simulations to identify opportunities for workers to optimize output and limit waste from substandard products.
Smart cities
Digital replica of the city with a virtual model of its roads, buildings and public spaces can help the city planners to better analyse and plan transportation systems, prepare for any calamities and warn the inhabitants about the pollution levels. Singapore has invested USD73 million for creating a 3D city model to build a more resilient city, enabling telecom companies to experiment with different wireless network systems for optimizing coverage, allow building owners to identify best places for solar panel installation, improve parks and evacuation routes. In India, the Andhra Pradesh government has decided its new capital, Amravati leveraging digital twin. The system will allow the city planners to evaluate design plans, monitor construction process, manage permit process, etc.
According to TechSci research report on “Global Digital Twin Market By Application (Manufacturing Process Planning, Product Design & Others), By End User Sector (Manufacturing, Energy & Utilities, Transportation & Others), By Region, Competition, Forecast & Opportunities, 2014 – 2024”, Global digital twin market is projected to grow from USD3.1 billion in 2018 to USD17.4 billion by 2024, exhibiting a CAGR of more than 33% during 2019-2024, on account of rising adoption of Industrial Internet of Things (IIoT) and use of virtual model for production line & automate the decision process. Some of the other factors expected to drive global digital twin market include rising adoption of connected devices and Industry 4.0, which refers to the application of mining and data analytics in manufacturing technologies. Moreover, rising technology demand in diverse sectors including energy & utilities, consumer goods and transportation would fuel digital twin market, globally, through 2024.
Major Technological Advancements in Digital Twin Technology
Simulation
The tools utilized for constructing digital twins are advancing in terms of power and sophistication. The task of designing and performing millions of simulation processes has become easier and efficient without overwhelming systems. With the growing adoption of digital twins, the number of vendors and range of options have increased, facilitating the advancement of digital twin technology. Even machine learning and functionality have also enhanced the depth and relevance of insights.
New Data Sources
Data retrieved from advanced real-time monitoring technologies such as Light Detection and Ranging (LIDAR) and Forward-looking Infrared (FLIR) has improved the productivity of digital twin simulations. Enabling continuous real-time monitoring, IoT sensors embedded in machinery or throughout supply chain can feed operation data directly into simulations.
Visualization
The data visualization tools have moved beyond basic dashboards as they have progressed into advanced visualization capabilities such as interactive 3D, Artificial Intelligence-enabled visualizations, real-time streaming, etc. These tools help to simplify the task of making digital twin simulations from a sheer amount of data.
Interoperability
The enhanced industry standards for communications between IoT sensors, operational technology hardware, and vendor efforts has improved the ability to integrate digital technology with real world on diverse platforms.
Instrumentation
With improvements in networking technology and security, the virtual models have become more granular, timely and accurate on real-world conditions. IoT sensors, both embedded and external are becoming compact, cheaper, powerful, and reliable, which is further helping to create detailed and precise digital twins.
Platform
Many software companies are making a significant investment in IoT, cloud-based platforms, and analytics, which can allow them to capitalize on the digital twin trend. The increased availability and access to powerful and inexpensive computing networks are the key enablers of digital twins.
Conclusion
With emerging technology, the potential of digital twin in every industry is endless. Digital twins have the potential to empower the shift to automated and iterative manufacturing to serve the modern day needs. Previously independently operating departments across operations, maintenance, sales, finance, and marketing can now leverage digital twins to access a unified source of real-world data for improved design, understand usage and adjust pricing. Going forward, digital twins will emerge as one of the crucial IT tools for the digitization of industrial marketplace and affect the social and political landscape of many countries.
According to TechSci research report on “Global IoT Device Management Market By Component (Software v/s Service), By Deployment Mode (On-Premise v/s Cloud), By Organization Size (Large Enterprises v/s SMEs), By Application (Smart Retail, Connected Health, Connected Logistics, Smart Utilities, Smart Manufacturing, Others), By Company, By Region, Forecast & Opportunities, 2026”, Global IoT device management market is expected to grow at a robust rate of around 22% during the forecast period. The global IoT device management market is driven by the increasing incidences of cybercrimes and cyber threats which has led to the increase in security concerns within enterprises. This in turn increases the need to deploy IoT solutions for ensuring organizational safety and security thereby driving the growth of IoT device management market during forecast period. Additionally, increasing penetration of communication and networking technologies further fosters the market growth. Furthermore, the extensive adoption of sensors is expected to create lucrative opportunities for the market growth through 2026. However, lack of IT infrastructure and finances in the emerging countries can hamper the market growth over the next few years. Moreover, lack of standardization also impedes the market growth.
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