Digital Twin Technology

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Digital Twin Technology

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Digital twins are digital representations of physical or potential physical objects, mainly used for digital testing, digital simulation, predictive maintenance, and analysis.

Examples of digital twin technology:
Digital Twin for Engineering Design, Simulation, and Testing
Digital Twin technology for Industry 4.0 can increase speed to market by enabling design engineers and industrial designers to virtually test and simulate products while still in the design phase.

For example, a design engineering team at a consumer goods manufacturing company can test and simulate bottle design and liquid flow for a shampoo bottle. This type of digital twin testing enables the team to see how a shampoo bottle will work virtually.

Using digital twin technology, engineers can do virtual tests instantly before real-world testing, shaving weeks or even months from the process. Then, using data from these tests, engineers can make changes immediately and test the product again, getting near-instantaneous results from design changes.

One executive from a fortune 100 manufacturer said that digital twin virtual testing for design cut their time to market for new product SKUs from three to 6 months to less than six weeks. In addition, digital twins have helped them address new consumer demands within weeks instead of months or years.

It also helps engineers test design changes from customer feedback, address design flaws, and release a final manufacturing revision within weeks. They can address customer concerns and fix faulty products much faster using digital twin technology. Digital twins enable actionable customer service, increasing customer retention, reducing recalls, and making for much happier customers.

Leveraging digital twin technology for design simulation is a competitive advantage for large enterprises, making it harder for smaller companies to disrupt them.

Digital Twin for Maintenance and Performance Analysis
GE Aviation is leading the charge in digital twin technology for maintenance and performance analysis. They are creating digital replicas (digital twins) for each new GE engine that goes into service. These digital twins collect and analyze real-world data.

Smart sensors collect data within each engine. Flight data like flight duration, weather, and altitude are also collected. One intercontinental flight has the potential to amass terabytes of data. All of that data is analyzed using AI and machine learning. A computer system uses the data to predict maintenance schedules, end of life for engine parts, and analytics for future design improvements.

Digital twin technology saves millions of dollars for GE Aviation customers by reducing downtime from previously unpredictable engine maintenance. It also increases airplane safety by determining potential fail points and mitigating them before a disaster occurs.

Manufacturing companies can harness the GE Aviation digital twin example for Industry 4.0 by creating digital twins for IIoT enabled manufacturing machines. Embedded sensors on the devices can communicate usage and wear and tear data to a control system. In addition, AI-driven data intelligence will analyze the digital twin data and production capacity, room temperature, scheduled maintenance, and downtime cost data for predictive maintenance.

Component Digital Twins

Component digital twins is a new use case of digital twin technology. Forward-thinking component manufacturers like Festo and Balluff deliver Industry 4.0 ready CAD models for the products they sell. Engineers can configure parts available from these companies and download them in a format that keeps the data’s fidelity. The component digital twins enable smart digital technologies downstream from engineering.

We discuss this topic in-depth in the ‘Data is the keystone of Industry 4.0’ section of this article.

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