Defining the Crucial Differences Between a Virtual Twin and Digital Twin
Lifecycle Insights
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Is a virtual twin any different from a digital twin? Dassault Systèmes thinks so, and here is why I agree with them.?
When people think of a digital twin, they believe it is a digital replica of the main product. That is okay, but one needs to analyze its usefulness during development and once the product is manufactured. This is one of the many areas where the virtual twin differentiates. According to Dassault? Systèmes, the virtual twin captures the history of a product's design and not just the final design. Not only that, it captures all the changes the product underwent and the reasons behind them. Moreover, a virtual twin captures the conversations between the design team members and gives excellent context to why engineers made those changes in the first place.
But why is this useful?
These advantages have elements of traceability and in-built knowledge capture in them. The virtual twin captures all the knowledge and innovation during product development. So when people leave an organization, the learning acquired stays behind. New members can quickly learn and get up to speed from the virtual twin.
Another advantage arises when the company envisions a new, updated product. In this situation, the virtual twin becomes an excellent starting point. The product development team can efficiently exploit existing information, reuse past designs, learn from past mistakes, and produce new designs. The result is less number of design iterations and faster development cycles.
But that is not all. A few more crucial differences exist between a virtual twin and a digital twin. Next, I will discuss how a virtual twin evolves when it collects and analyzes actual field data from its physical twin.
How Does the Virtual Twin Evolve?
Unlike a generic digital twin, a virtual twin is not a static representation. The virtual twin evolves by continuously integrating new data and insights into its models (typically simulation models). It also leverages analytics and machine learning techniques to improve accuracy and predictive capabilities. But where does the virtual twin get its data?
The virtual twin integrates data from various sources, such as sensors, internet of things devices, and product design and simulation software, to create a digital replica of a physical product or process. It then uses this data to simulate the physical system's behavior under different conditions and scenarios. The virtual twin continuously updates its simulation models and machine learning algorithms to improve its accuracy and predictive capabilities as it receives more data and feedback from the real world. It also enables collaboration between stakeholders in a product's design, development, and operation, allowing them to share data, insights, and simulations. This collaborative approach helps to ensure that the virtual twin stays up-to-date with the latest data and insights, enabling it to evolve continuously.
What does this mean to a product development company using the virtual twin? I can think of a few. The product company can:
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The virtual twin enables engineers to make better decisions and products to perform better for consumers. Two essential elements that enable this are collaboration capabilities and integration with other technologies.?
Collaboration capabilities
The virtual twin experience allows all stakeholders to collaborate and exchange relevant information within the same interface. The communications are organized as a part of the virtual twin, enabling users to find relevant information quickly and make informed decisions.
Integration with other technologies?
The virtual twin is tightly integrated with other Dassault Systèmes technology, such as?3d design, physics-based simulation, systems engineering, manufacturing planning, manufacturing operations, and data analytics. This integration provides customers with a comprehensive solution for modeling, simulating, manufacturing, and analyzing complex products. The integration allows engineers to validate product ideas quickly in a virtual environment, devise strategies for manufacturing, launch products to market faster, and create a brand associated with superior quality and performance.
What Happens When New Technologies Emerge
As more technologies emerge, the virtual twin can integrate with those to deliver a superior user experience. The architecture of the virtual twin allows integration with different solutions, which need not be from Dassault Systèmes. As with any dynamic system, the virtual twin experience will expand, and users will see more technologies and solutions as a part of the virtual twin experience.
Arvind Krishnan is an senior industry analyst at Lifecycle Insights covering product design and simulation programs for research and publications. His expertise spans mechanical, electrical, and electronics domains, offering powerful insights to companies transitioning to smart, connected products and adopting simulation driven product development. His twenty-year career has concentrated on advocating for technologies that make a tangible impact on organizational performance.
Lifecycle Insights conducts research and publishes guidance for engineering executives. Follow us for more on digital transformation in product development.