What is a Digital Twin?
A digital twin is a dynamic, virtual representation of a physical object, system, or process. This digital model is continuously updated with real-time data from its physical counterpart, enabling simulations, monitoring, and analysis to optimize performance and predict potential issues.
Originating from NASA ↗ and their efforts to improve spacecraft simulations in the 1960s, the concept has evolved to encompass various applications across industries, from manufacturing to urban planning.
The role of digital twin
Digital twins provide a comprehensive, real-time view of physical assets. They enable manufacturers to simulate and test different scenarios without disrupting actual operations, leading to improved design, efficiency, and maintenance. For instance, in the automotive industry, digital twins allow engineers to predict the performance of new vehicle models under various conditions, thereby reducing the need for physical prototypes and accelerating time-to-market.
In manufacturing, digital twins help optimize production lines by identifying bottlenecks and inefficiencies. They can simulate the impact of changes in the production process, such as the introduction of new machinery or modifications in workflow, ensuring that these changes lead to desired outcomes.
Additionally, digital twins facilitate predictive maintenance by analyzing data from sensors embedded in machinery to forecast potential failures and schedule timely repairs, thereby minimizing downtime and extending the lifespan of equipment.
What are the benefits of a digital twin strategy?
Improve product quality
Before investing in prototyping or physical development, test and validate your design, and your production processes. This new capability accelerates the development of better, more sustainable products by greatly improving risk assessment and ensuring production reliability.
Reduce time-to-market
By simulating and testing products virtually, you can significantly cut down the time required for physical prototyping and validation, speeding up the development process.
Improve risk assessment
Engineers can simulate unexpected scenarios or disruptions in production processes to analyze system responses and develop mitigation strategies. This proactive approach reduces the likelihood of costly production delays or failures while enhancing overall system reliability.
Real-time monitoring
IoT-connected sensors provide continuous data streams that allow businesses to analyze performance remotely, enabling timely interventions to prevent failures. Predictive analytics help schedule maintenance more accurately, reducing downtime and improving OEE (overall equipment effectiveness).
Reduce costs
Fewer physical prototypes mean reduced material waste, reduced maintenance and service costs, and lower R&D expenses. Predictive maintenance capabilities, with the support of machine learning and AI, help prevent unplanned downtime and extend equipment life, cutting maintenance costs and improving productivity.
What’s the best way to get started with a digital twin?
Implementing a digital twin strategy requires a structured approach that aligns technology, processes, and organizational goals to maximize its potential.
Gather data from physical assets using sensors, IoT devices, and other data sources. This data includes parameters like temperature, pressure, and operational status.
Create a virtual model of the asset using digital twin software that accurately reflects its physical counterpart.
Connect the digital twin to real-time data sources and validate its accuracy by comparing simulation results with actual performance data.
Use the digital twin to analyze data, run simulations, and develop strategies for optimizing performance and maintenance.
Need help with Digital twin?
Matthias Hogberg and team are on-hand to provide tailored guidance and support with a deep knowledge of the full Dassault Systèmes portfolio. Reach out for a free consultation today.
Essential software for industrial digital twin
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