Sector aeroespacial y defensa
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A digital twin is a virtual representation of a physical product or process, used to understand and predict the physical counterpart’s performance characteristics. Digital twins are used throughout the product lifecycle to simulate, predict, and optimize the product and production system before investing in physical prototypes and assets.
By incorporating multi-physics simulation, data analytics, and machine learning capabilities, digital twins are able to demonstrate the impact of design changes, usage scenarios, environmental conditions, and other endless variables – eliminating the need for physical prototypes, reducing development time, and improving quality of the finalized product or process.
To ensure accurate modelling over the entire lifetime of a product or its production, digital twins use data from sensors installed on physical objects to determine the objects’ real-time performance, operating conditions, and changes over time. Using this data, the digital twin evolves and continuously updates to reflect any change to the physical counterpart throughout the product lifecycle, creating a closed-loop of feedback in a virtual environment that enables companies to continuously optimize their products, production, and performance at minimal cost.
The potential applications for a digital twin depend on what stage of the product lifecycle it models. Generally speaking, there are three types of digital twin – Product, Production, and Performance, which are explained below. The combination and integration of the three digital twins as they evolve together is known as the digital thread. The term "thread" is used because it is woven into, and brings together data from, all stages of the product and production lifecycles.
A production digital twin can help validate how well a manufacturing process will work on the shop floor before anything actually goes into production. By simulating the process using a digital twin and analyzing why things are happening using the digital thread, companies can create a production methodology that stays efficient under a variety of conditions.
The production can be optimized even further by creating product digital twins of all the manufacturing equipment. Using the data from the product and production digital twins, businesses can prevent costly downtime to equipment – and even predict when preventative maintenance will be necessary. This constant stream of accurate information enables manufacturing operations that are faster, more efficient, and more reliable.
Smart products and smart plants generate massive amounts of data regarding their utilization and effectiveness. The performance digital twin captures this data from products and plants in operation and analyzes it to provide actionable insight for informed decision making. By leveraging performance digital twins, companies can: