DIGITAL TWINS

 Hi Folks 

I have been to a seminar of a mechanical topics as it is defined from todays topics i.e.,

Digital Twins

I am just posting what i understood from the seminar regarding the topic so here it is...

WHAT IS A DIGITAL TWIN?

The concept of digital twins is not exactly new – it was first presented by Dr Michael Grieves in 2002, and, prior to that, NASA had been using complex simulations of spacecraft for decades. But thanks to the explosion of the internet of things (IoT), and the subsequent lowering costs of associated technologies, digital twins are now more accessible than ever.

A digital twin is a virtual representation of a real-life object or device. If you think that sounds a lot like 3D renderings of computer-aided design (CAD) models, you’d be right. But where digital twins differ crucially from simple 3D models is that they also combine the physical elements and the dynamics of how that object or device operates in the real world. In other words, you can see, almost in real time, precisely how an object or device responds throughout its lifecycle. Just as an asset drifts in response to factors like the weather, the ambient temperature, operator idiosyncrasies, and so forth, so too does its digital twin.

Digital twins do this by combining data collected from sensors on the device, with knowledge related to the design, build, operation and servicing of the physical twin. Already, just from this data, you have a rich, highly detailed picture of the asset. Intelligence, in the form of analytics, physics, and machine learning, is then built on top of the data, acting as the “brain” of the digital twin, and making things like predictive modelling, optimization and early warning systems possible.

BENEFITS OF DIGITAL TWINS

The benefits of digital twins are incredibly far-reaching, and extend throughout a product’s entire lifecycle, from design, to build, and finally to operation.

Here are just some of the benefits of digital twins:

1.      Improved design: Digital twins allow you to quickly test designs using simulations, without the need for costly prototypes.

2.      Improved build: Digital twins allow you to project how a change in the manufacturing process might impact things like efficiency, quality and yield.  

3.      Better early detection and warnings: Digital twins can quickly alert you to any abnormalities or failures in the asset, allowing you to address before it becomes a major (and potentially costly) problem.

4.      Predictive maintenance: Digital twins not only gives you real-time insight into how an asset is performing, but it also allows you to model your interventions, so you can see the full-scale of their impact and minimise downtime losses.

5.      Aggregated data: Aggregated data is valuable. As Dimitri Volkmann of GE notes: “If your organisation is monitoring multiple systems of the same type of assets, for instance a fleet of jet engines (each of which has an individual digital twin), you can start to learn from all of them as a cohort, find similar patterns or trends, and that analysis can lead to refining models for higher fidelity in the future.”

6.      Post-manufacturing visibility of products: For many products, once they leave the factory, there is no more insight into how that product is being used by consumers – until something goes wrong, that is. Digital twins can change that, by giving manufacturers visibility into their real-world usage, allowing them to further optimise the product, predict when it might be in need of service, and quickly fix any problems that do arise.

 

ESSENTIAL COMPONENTS OF A DIGITAL TWIN

According to Chris O’Conner, General Manager, Internet of Things Offerings for IBM, if you want to implement digital twins in your business, these are the 3 essential capabilities you must have if you want to reap their full benefit.

Analytics at every step: A digital twin deals with a staggering amount of data, and its effectiveness is reliant on whether this data is:

·         real-time,

·         operational,

·         high-quality, and

·         predictive-orientated in its nature.

Open and federated data: The data has to be accessible from several sources, and be pulled together into a federated model, rather than being centralised in proprietary systems.

Applied industry context: Applying industry context is essential to getting maximum value out of a digital twin. In fact, it is possible to have two different digital twins for the same product that is being used in two different industries, because of how the industry context is applied to the twin.

The future of digital twins

As digital twins become more advanced and more widespread, what we’ll see is digital twins interacting with each other, creating models of highly complex systems. We’ll have digital twins for entire cities, and even human beings.

“Over time, digital representations of virtually every aspect of our world will be connected dynamically with their real-world counterparts and with one another and infused with AI-based capabilities to enable advanced simulation, operation and analysis,” says vice president and Gartner Fellow David Cearley. “City planners, digital marketers, healthcare professionals and industrial planners will all benefit from this long-term shift to the integrated digital twin world.”