“Real data, coupled with digital simulations of digital twin products, provides valuable insights to help companies identify and solve problems before prototypes go into production and manage products in the field,” says Alberto Ferrari, Senior Director of Business Unit Model-Based Digital. The Streaming Capability Center at Raytheon.
“As the saying goes:“ All the models are wrong, but some of them are useful, ”says Ferrari. “Digital twins, backed up by data as real facts, are a way to identify models that are truly useful for decision-making.”
This concept began to gain momentum, the market for technologies and tools for digital twins grew by 58% annually reach $ 48 billion by 2026, up from $ 3.1 billion in 2020. Using technology to create digital prototypes saves resources, money and time. However, technology is also being used to model much more, from urban populations to energy systems and the deployment of new services.
Take manufacturers as diverse as Raytheon and a Swedish distillery Vodka Absolutwho use this technology to develop new products and optimize production processes, from the supply chain to production and ultimately to recycling and disposal. Singapore, London, and several cities on the Texas Gulf Coast created digital twins of their communities to address various aspects of city governance, including simulating traffic on city streets, analyzing construction trends, and predicting the impact of climate change. And companies like Bridgestone and Zipline, an unmanned aerial vehicle service provider, are using this technology, combined with operational data, to launch new services.
Companies have implemented digital twins as part of their digital transformation, a way to model performance, identify bottlenecks, and manage services more effectively. Any digital company initiative must investigate whether certain aspects of its product, operations, or environment can be modeled in order to gain insight.
Design and manufacturing simulation
At the heart of modern digital twin technologies are computer-aided design (CAD) and computer-aided engineering tools developed over three decades ago. These software systems allowed engineers to create virtual simulations to test product design changes. Engineers developed a product component, such as an airfoil, on a computer, and then instructed a modeler or sculptor to make the product out of clay, wood, or standard components for physical testing.
Today in the prototyping process, the prototyping stage has been moved to a later stage, as significant increases in computing power and storage allow prototyping not only the entire product, but also integrate other information, such as information on raw materials supply. materials, components required for production, and operation of the product in the field.
“If you look at these 30-year-old CAD and engineering tools and squint your eyes a little, you can see they were digital twins,” says Scott Buchholz, CTO for government and government services and director of research for emerging technologies at Deloitte. Consulting. “As computing power and storage grew, so did the ability to run useful simulations, and we moved from low fidelity rendering to high fidelity simulation.”
As a result, digital twin technology has taken over many industries. Manufacturers of high-value vehicles and infrastructure products benefit from shorter design and development cycles, with aerospace companies, car manufacturers and urban planning agencies being the first to implement them. However, startups are also adopting a “model first” mentality in order to quickly make product improvements.
Key benefit: The digital twins have pushed physical prototyping much further down the design pipeline. “Some companies with zero-prototyping initiatives are looking to eliminate prototyping steps altogether and ensure direct implementation into production,” said Nand Kochkhar, vice president of automotive and transportation at Siemens Digital Industries Software.
This is a significant shift from past times. “The typical product development lifecycle was six to eight years,” says Kochkhar of the automotive industry. “The industry has worked on this and now they have an 18 month or 24 month life cycle. Now the automotive industry is more dependent on software, which becomes a determining factor in the life cycle. “
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