Digital Twins

 A digital twin is a digital copy of an actual physical product, process, or ecosystem that can be used to run virtual simulations, using data to update and change the digital copy to reflect any changes in the real world.

The idea behind a digital twin is to let us see what might happen if we were to make certain adjustments in real life. These adjustments can be trialed on the digital twin without having to test potentially expensive changes on the real-world counterpart.

Creating a digital twin requires different elements, including:

Sensors capturing operational behaviors of assets and processes (vibration, temperature, pressure, etc.), alongside their functioning environments (air temperature, humidity, etc.)

Communications networks providing secure and reliable data transfer from physical devices to the digital world

A digital platform that serves as a modern data repository pooling and storing shop floor sensor data with high-level business data (e.g. MES, ERP). By combining these data sources, actionable insights can be derived for data-driven decision-making – using advanced AI/machine learning algorithms.

First realized in the aerospace industry, digital twins are now gaining traction across industrial verticals. You can build a digital twin of almost everything regardless of its size – from single components and assets (rotors, turbines, pipelines, etc.) to complex processes and environments (production lines, manufacturing plants, wind farms, etc.). The level of sophistication and detail of your digital twin models depends on the availability and maturity of your IT infrastructure.

3 Applications of Digital Twins for Industry 4.0

Digital twin technology renders unprecedented visibility into assets and production to spot bottlenecks, streamline operations and innovate product development. Below are the three major applications of digital twins for Industry 4.0.

Predictive Maintenance: Gaining a holistic view of the health and performance of equipment, companies can immediately detect anomalies and deviations in its operations. Maintenance and replenishment of spare parts can be proactively planned to minimize time-to-service and avoid costly asset failures. For OEMs, predictive maintenance using Digital Twins can provide a new service-based revenue stream while helping improve product reliability.

Process Planning and Optimization: A digital footprint ingesting sensor and ERP data of a manufacturing line can comprehensively analyze important KPIs like production rates and scrap counts. This helps diagnose the root cause of any inefficiencies and throughput losses, thereby optimizing yields and reducing wastes. Taking it one step further, rich, integrated historical data on equipment, processes, and environments can enable downtime forecasting to improve production scheduling.

Product Design and Virtual Prototyping: Virtual models of in-use products provide comprehensive insights into usage patterns, degradation point, workload capacity, incurring defects, etc. By better understanding a product’s characteristics and failure modes, designers and developers can correctly evaluate product usability and improve future component design. Similarly, OEMs can deliver customized offerings for different groups of customers based on specific usage behaviors and product implementation contexts. Digital twin technology additionally aids in developing virtual prototypes and running robust simulations for feature testing based on empirical data.

Thanks!

Vedant Saraf