“A digital twin is a dynamic virtual representation of a physical object or system, usually across multiple stages of its lifecycle. It uses real-world data, simulation or machine learning models, combined with data analysis, to enable understanding, learning, and reasoning. Digital twins can be used to answer what-if questions and should be able to present the insights in an intuitive way.”**
** Courtesy: The IBM UK Technical Consultancy Group (TCG), an affiliate of IBM’s Academy of Technology (AoT),
A digital twin is a dynamic representation with a great deal of detail that replicates data from its physical counterpart in real time. It has a variety of uses and advantages that have the potential to greatly influence the future in a number of ways.
It bridges the gap between the actual and virtual worlds by collecting data in real time through implanted sensors.
Among the first institutions to use this technology for space exploration missions was NASA.
The current vast area of corporate opportunity is digital twins. Analyzed data from virtual copies can be used to forecast business occurrences in the future.
Given that digital twins are designed to represent a product’s whole life cycle, it is not unexpected that they are now used in every phase of production, helping with product creation, design, production, and every stage in between.
Digital twin technology can be used to enhance manufacturing processes.
Virtualized physical manufacturing things are fluidly and closely integrated into both the real and cyber environments as digital twin models.
Because they make predictions about the future of the manufacturing process rather than evaluating its past, digital twins have enormous business potential.
Organizations can minimize downtime and maximize efficiency by anticipating when physical assets need maintenance or repairs by regularly monitoring the data in the digital twin.
Digital twins can guarantee that requirements that are recorded at the beginning of a product’s lifecycle are kept up to current, checked, and validated as the product develops, is manufactured, put into use, and eventually retired, decommissioned, and recycled.
A. In Product Design and Prototyping:
In order to test and improve concepts before producing physical prototypes, engineers and designers might employ digital twins to construct virtual prototypes of items. As a result, less physical prototyping is required, saving money and effort.
B. In Process Optimization:
Digital twins assist in the real-time identification of bottlenecks, inefficiencies, and areas for improvement by mimicking industrial processes.
Manufacturers may optimize production and save operating expenses by making data-driven decisions.
C. Predictive Maintenance:
Digital twins are used to continuously track the performance and state of production equipment.
By identifying anomalies and early warning indicators of wear or malfunction, regular maintenance can be performed, reducing unscheduled downtime.
D. Resource Management:
Digital twins can help manufacturers monitor and optimize the use of resources, such as electricity and raw materials, which can save costs and increase sustainability.
E. Supply Chain Visibility:
Throughout the production process, the movement of raw materials, work-in-progress, and completed goods can be monitored using digital twins.
By increasing supply chain visibility, this facilitates inventory management and guarantees on-time delivery.
F. Customization and Personalization:
Digital twins enable mass customization and personalization of products by manufacturers, meeting the demands of individual customers while preserving effective production procedures.
G. Remote Monitoring and Control:
Digital twins provide real-time data that allows for remote monitoring and control of production processes.
This is especially helpful for operations that are dispersed across multiple sites and worldwide supply networks.
H. Reduced Downtime and Scrap:
Digital twins help decrease downtime and the manufacturing of defective products by anticipating equipment breakdowns and streamlining procedures, which raises overall efficiency.
I. Documentation and Compliance:
By capturing and storing data about production processes, digital twins can aid in documentation and compliance by guaranteeing traceability and conformance to industry standards.
J. Continuous Improvement:
By utilizing digital twins to provide real-time feedback and access to previous data, manufacturers can continuously enhance their processes and product quality.
Digital Twin in Healthcare:
Individualized healthcare and treatment strategies are made possible by the deployment of digital twins to represent specific patients or organs. Prior to carrying out operations on real patients, surgeons can also rehearse them on a virtual model. To facilitate individualized care, surgery planning, and medical research, digital twins can be used to represent specific patients, organs, or medical equipment.
Key insights can be produced and a range of health indicators can be tracked using the sensor-generated data system.
To establish a comprehensive picture of a patient’s health and identify correlations in their symptoms, patient monitoring methods can be combined in a digital twin.
Digital Twin in Smart City Management:
Cities or entire urban areas can be represented by digital twins, which can be used to optimize infrastructure, traffic flow, energy use, and emergency response. Digital twins are used by city planners to maximize public services, energy utilization, traffic control, and urban infrastructure.
Digital twins, which can display 3D and 4D spatial data in real time and integrate augmented reality systems into constructed environments, are a great help to civil engineers and other urban planners.
The use of geographic digital twins in urban planning has become more commonplace as the Smart Cities movement has increased demand for digital technologies. In order to model urban environments, these digital twins are frequently presented as interactive platforms that record and show real-time 3D and 4D geographical data.
Digital Twin in Environmental Monitoring:
Digital twins can help with environmental monitoring, conservation initiatives, and disaster planning by simulating ecosystems and natural resources.
Digital Twin in Aerospace and Defense:
Digital twins are used in the aerospace industry for maintenance, simulation, and aircraft design. They back mission planning and training in defense. Predictive maintenance, performance testing, and the design of vehicles and aircraft are all made easier by digital twins.
With real-time visibility into inventory control, logistics of transportation, and the flow of goods, digital twins can be used to optimize supply chain operations. Enabling participants in a supply chain to view the digital twin of a good or asset can improve connectedness between partners and consumers. These partners can then use the digital twin to quickly ascertain the product’s current state.
Digital Twin for Internet of Things (IoT) Integration:
Digital twins can easily interface with Internet of Things (IoT) devices, enabling more extensive control and monitoring of physical assets as the IoT expands. The Internet of Things (IoT) devices and sensors are frequently intimately connected with digital twins, facilitating smooth data transfer between the real asset and its digital twin.
Digital Twin in Automotive Industry:
The development and testing of autonomous vehicles greatly benefit from the use of digital twins, which make the systems safer and more dependable.
Automobiles encompass a wide range of complicated systems, and digital twins are widely employed in automotive design to enhance vehicle performance and boost production efficiency.
Digital twin technology has helped the automotive sector. Utilizing current data, digital twins are applied in the automotive sector to streamline procedures and lower marginal costs.
Automotive engineers utilize digital twin technology in conjunction with the company’s analytical tool to examine how a particular car is driven. This is one particular application of digital twin technology in the automotive sector. By doing this, they can recommend adding new features to the vehicle that would otherwise be impossible to implement in such a short amount of time and lower the number of auto accidents on the road.
Not only can digital twins be created for individual vehicles, but also for the entire mobility system. In this system, people (such as drivers, passengers, and pedestrians), vehicles (such as automated and connected vehicles), and traffic (such as traffic networks and infrastructures) can all consult their digital twins, which are set up on edge or cloud servers, to make decisions in real time.
Digital Twin in Power-generation equipment:
Digital twins are a great tool for large engines, such as locomotives, jet engines, and power-generating turbines. They are particularly useful for helping to schedule routine maintenance.
Digital Twin for Larger Structures and their systems:
Digital twins can be used to improve massive physical structures, such skyscrapers or offshore drilling platforms, especially during the design phase. furthermore helpful for creating the systems that run inside those frameworks.
Digital Twin in Energy Management:
Better energy management in commercial and residential buildings is made possible by digital twins, which also save expenses by optimizing energy use. Digital twins help in the energy sector by monitoring renewable energy installations, streamlining grid management, and optimizing power plants. With the use of digital twins, supply chain operations—from transportation to warehousing—can be tracked in real time for increased efficiency and visibility.
Digital Twin in Training:
Military, medical, and industrial training are just a few of the contexts in which digital twins are employed to create safe and effective learning environments.
The value of a digital twin as a training tool is rapidly expanding as assets become more sophisticated and seasoned knowledge workers approach retirement. Long-term apprenticeships or mentoring are no longer necessary when all the information is accessible to a new user with a digital twin. Of course, new team members will frequently require assistance, but the digital twin has frequently shown to enable teams to make the right corrections the first time.
Digital twins are used as training grounds for new hires and as a means of improving the skills of current personnel. By practicing their jobs in a virtual setting, operators can increase their skill and lower the number of mistakes they make on the shop floor.
Digital Twin in Quality Control:
By tracking and evaluating manufacturing data in real time, digital twins assist quality control procedures by enabling the early identification of flaws and irregularities.
Real-time monitoring and analysis of manufacturing processes is made possible by digital twins. Early detection of any deviations or flaws enables prompt remedial action to preserve product quality.
Digital Twin for Sustainability Development:
Digital twins of products can be especially useful for enhancing sustainability initiatives. These digital twins can assist companies in cutting down on the amount of material utilized in product design and enhancing product traceability to cut down on waste generated in the environment.
Digital Twin in Project planning:
In order to help with contingency and resilience management and make sure the plan is achievable, a digital twin for project planning can be used to compare various lifecycle plans based on the effect from other digital twins as they evolve.
Digital Twin in Reliability Engineering:
The evolution of Industrial IoT solutions over the past few years has made it increasingly possible to digitally reflect sensor information from a real-world instance of an asset; this is not a novel concept, but it has improved in terms of scalability, security, cost, and resilience. Reliability engineers are better able to predict and manage risk based on high-quality data rather than assumptions based solely on experience when they have the ability to monitor asset performance in as close to real-time as is necessary. This allows them to optimize asset behavior, increase system efficiency, and improve overall asset performance.
Digital Twin for Real-time Decision Making:
Using digital twins, decision-makers may quickly ascertain the effects of modifications made to an asset at any stage of its lifecycle. What effects, for instance, would a material alteration have on the project plan model, the mass and center of gravity design model, the cost model for the total financial impact, and so on. Instead of going through the process of making physical prototypes, an organization can use the digital twin to run simulations to answer “what if” questions, often repeatedly, with tweaks to the settings.
Digital Twin in Decommissioning Resources:
Because there are limited quantities of some resources in the world, there has been a lot of attention paid to how assets are recycled, decommissioned, or scrapped in order to promote a circular economy in recent years. Because steel is a limited resource, for instance, major steel producers place a lot of emphasis on knowing where their products are being used, for how long, how they will be maintained, and what condition they will be in when their first life is coming to an end. This is done to ensure that their products can be reused, possibly at a lower grade, for future products.
The digital twin can be used to enhance reporting and regulatory compliance in light of worldwide initiatives to monitor plastics and other hazardous materials to ensure safe consumption and disposal.
What Can Digital Twin Technology do?
- Real-time data from sensors, Internet of Things (IoT) gadgets, and other sources linked to the physical asset they represent can be captured by it. The purpose of this data is to correctly reflect the asset’s real-world state.
- Digital twins offer an extremely thorough and precise replica of the physical object, complete with dimensions, specs, performance statistics, and historical details.
- By using these virtual representations, simulations and analysis may be conducted to determine how the physical asset will respond to certain circumstances, situations, or alterations. For testing and optimization, this is helpful.
- Predictive Capabilities: Digital twins make predictive analytics possible by utilizing real-time monitoring and historical data. They can therefore foresee problems, provide suggestions for enhancements, and assist in decision-making.
Challenges Associated with Digital Twin:
For accurate simulation and realistic visualization, digital twins depend on three-dimensional (3D) representation. Still, the conversion process is the biggest obstacle. Converting a 2D model to a 3D equivalent requires a thorough grasp of spatial dimensions, accuracy, and visual fidelity. This problem can be solved by applying 3D point clouds and using CAD.
A new solution may need to be time- and money-consuming to adapt if the Digital Twins’ new platforms and technologies aren’t seamlessly linked with the existing tech components. By automatically reflecting data collected and evaluated by the digital twin in the ERP system and vice versa, the use of ERP aids in problem resolution.
With the inclusion of digital twins, privacy and security concerns are raised.
Challenges Associated with Digital Twin:
For accurate simulation and realistic visualization, digital twins depend on three-dimensional (3D) representation. Still, the conversion process is the biggest obstacle. Converting a 2D model to a 3D equivalent requires a thorough grasp of spatial dimensions, accuracy, and visual fidelity. This problem can be solved by applying 3D point clouds and using CAD.
A new solution may need to be time- and money-consuming to adapt if the Digital Twins’ new platforms and technologies aren’t seamlessly linked with the existing tech components. By automatically reflecting data collected and evaluated by the digital twin in the ERP system and vice versa, the use of ERP aids in problem resolution.
With the inclusion of digital twins, privacy and security concerns are raised.
The three-step approach to building and scaling a digital twin:
- Draw up a plan. An organization’s plan should specify the kinds of twins it will seek, the best order to establish them in terms of value and reusability, how their skills will develop, and the ownership and governance arrangements.
- Construct the initial digital twin. Over the course of the following three to six months, a project team constructs the base digital twin. The first step in this phase is putting together the core data product, which lets data scientists create visualizations and establish one or two pilot use cases.
- Increase capacity. An organization can increase its capabilities by adding more data layers and analytics to enable new use cases after a digital twin is operational. At this point, companies usually use AI and sophisticated modeling approaches to progress their twins from merely representing assets, people, or processes to offering simulations and prescriptions.
Concluding Note:
The potential to create individualized models for patients that are constantly modifiable based on monitored health and lifestyle indicators is made possible by the availability of digital twin technologies. In the end, this may result in a virtual patient with a thorough description of each patient’s health based on their own records as well as past ones. Additionally, the digital twin makes it possible to compare a person’s information to the population in order to more easily identify intricate patterns.
To sum up, digital twins are an effective instrument that improves productivity, judgment, and competitiveness in a range of businesses. In the age of contemporary industry, their real-time data integration, simulation capabilities, and predictive analytics make them indispensable.