A digital twin is a digital representation that provides the elements and dynamics of how a device or ecosystem operates and lives throughout its life cycle. Digital twins are useful for simulating the capabilities of machine tools in a safe and cost-effective way, as well as identifying the root causes of problems occurring in physical tools or infrastructure. If a physical machine tool breaks down or malfunctions, engineers can evaluate the digital traces of the digital twins’ virtual machines for diagnosis and prognosis.
The digitisation of nearly every industry type is helping to fuel the demand for twinning platforms, as is the desire to monitor, control, and model the future behaviour of real-world equipment, systems, and environments. However, like any technology, digital twins must be understood and accepted by several different stakeholders, from the operations workers up to the C-suite. Meanwhile, vendors are highlighting their expertise in analytics and demonstrating domain expertise with specific industry verticals. Some are also spotlighting their experience with incorporating artificial intelligence (AI) and machine learning (ML) technologies, which can provide the ability to model future behaviour via digital twins. These technologies are anticipated to drive the functionality of digital twins beyond simply being enhanced analytics tools.
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