Two powerful tools that have emerged to address the needs of optimizing operations and enhancing product development of companies are digital twin and simulation. At issue is “Are Digital Twins and simulations the same?”. While both serve valuable purposes, the purpose of the digital twins stretches beyond simulations, and understanding their key differences can help companies harness their true potential. In this blog post, we will explore the main distinctions between digital twins and simulations and highlight the unique advantages they offer.
Digital Twin
A digital twin is a virtual model of a physical product or process that includes real-time data. Unlike simulations, digital twins are not limited to design testing alone. They provide a comprehensive view of a product’s entire lifecycle, capturing data from various stages, including manufacturing, usage, and maintenance. In other words, they are used to the whole design-execute-change-decommission lifecycle in real-time. This wealth of real-time information enables businesses to monitor and analyze the performance of their products in the real world, making informed decisions based on accurate and up-to-date data. This helps prevent mistakes, enables proactive maintenance, and reduces the occurrence of accidents or incidents
Imagine a solution where real-world data from sensors is gathered and organized into a virtual model called a digital twin. This digital twin provides a user-friendly, 3D visual representation of the current status of large-scale projects, complex processes and equipment, operational assembly lines, and even patients under medical care.
In a fast-paced world where time is crucial, digital twins provide valuable information that can prevent problems, optimize manufacturing, enhance maintenance strategies facilitate data-driven decision-making, and improve overall efficiency across various business workflows.
Also visit: What is AIoT (Artificial Intelligence of Things)?
Simulation
Simulations, often based on computer-aided design (CAD), are powerful tools for testing different scenarios within predefined parameters. They allow designers to visualize and predict the behavior and performance of a product before it is physically built. Simulations are particularly useful during the design phase, helping designers identify potential flaws, optimize performance, and reduce development time and costs. By illustration, architects and bridge designers utilize simulation software to test their models. This software can accurately replicate the impact of pressure on various materials, enabling designers to refine their blueprints and make necessary improvements before construction begins.
By simulating different scenarios, designers can ensure the structural integrity and safety of their designs, leading to more efficient and reliable structures. Besides design testing simulations, Engineers utilize computer-aided design (CAD) to simulate various scenarios beyond design testing. These simulations come in different forms such as discrete event simulations, deterministic simulations, and stochastic simulations. It’s important to note that simulations can only be conducted if a company has digitalized its processes. This means engineers need a digital representation of actual assets to run simulations. Once they have this digital model, they can manipulate different variables to test product features in a virtual environment. However, simulations are limited to theoretical scenarios and lack the real-time feedback that digital twins offer.
Key Differences of Digital twin and simulation
Although simulations and digital twins share the commonality of using digital models to replicate real-world products and processes, they have distinct characteristics that set them apart.
Scope of Use
One key distinction between digital twins and simulations lies in their scope of use. While simulations primarily focus on design testing, digital twins extend their reach throughout the entire lifecycle of products, meaning that they play an essential role in various stages and areas of a business workflow. The versatility of a digital twin allows it to be utilized not only in design testing but also in improving processes and making informed decisions across the entire business. By leveraging the data from a digital twin, companies can optimize operations, enhance efficiency, and make better business choices that positively impact their overall performance.
Possible vs. Actual
A simulation replicates what might happen to a product. A digital twin, however, replicates what is happening to a specific product in the real world. In a simulation, any changes made are based on the designer’s imagination. However, with a digital twin, the designer can see if performing as intended and make improvements based on real-time feedback. This applies not only to physical assets but also to other applications like manufacturing processes. Using real-time data, a digital twin allows businesses to adapt to changing demands, requirements, and conditions. The main difference is that a simulation is theoretical, while a digital twin is based on actual and specific information.
Static to Active
Another significant advantage of digital twins over simulations is their ability to evolve and adapt. Simulations are static models that require manual input from designers to introduce changes or improvements and The static model can only provide information about that specific design. New designs need engineers to build another simulation from scratch. Digital twins, on the other hand, continuously collect and analyze real-time data, allowing for active simulation. This means that digital twins can change and develop over time, providing a more accurate representation of the actual product or process.
For instance, simulation software can simulate the entire lifecycle of a product, providing designers with valuable insights that a basic simulation cannot offer. This means designers can observe how a product will perform and evolve, taking into account factors such as wear and tear, environmental conditions, and usage patterns. By simulating the complete product lifecycle, designers can make informed decisions and optimize their designs for long-term performance and durability. This dynamic evolution enables businesses to identify performance gaps, detect anomalies, and implement improvements based on real-world usage.
Conclusion
Digital twins and simulations are powerful tools that have revolutionized product development and process optimization. While simulations excel in design testing, digital twins offer a broader scope, incorporating real-time data and enabling businesses to make informed decisions throughout a product’s lifecycle. Their ability to adapt and evolve based on real-world feedback makes digital twins valuable for enhancing performance, improving processes, and driving innovation. By leveraging the unique advantages of both digital twins and simulations, businesses can unlock new possibilities and stay ahead in today’s competitive landscape.
