Ever made a machine? If yes, then how many attempts it took to make it function flawlessly, to make it the ideal one? I guess number of times you might have tried. It’s not only you but every manufacture faces this troublesome situation.
So to provide solution for such critical situation a technology called ‘Digital Twin’ has been introduced.
A Digital Twin of any device/system is a working model of all component integrated and mapped together using physical data, virtual data and interaction data between them to make a fully functional replica of the device/system and that too on a digital medium. This digital twin of the physical system is not intended to outplace the physical system but to test its optimality and predict the physical counterparts’ performance characteristics.
The concept and model of the Digital Twin was officially put forward in 2002 by Dr. Michael Grieves as the conceptual model underlying Product Lifecycle Management.
The concept was being practiced since the 1960s by NASA. They used basic twinning ideas for space programming at that time. They did this by creating physically duplicated systems at ground level to match the systems in space.
A Digital Twin consists of three distinct parts: The physical part, the Digital Part and the connection between the two. The connection here refers to the data that flows from physical products to the digital/virtual product and information that is being available from the digital environment to the physical environment.
For the creation of a digital twin of any system, the engineers collect and synthesise data from various sources including physical data, manufacturing data, operational data and insights from analytics software. The sensors are connected to the physical product that helps to collect data and send it back to the digital twin, and their interaction helps to optimise the product’s performance using a maintenance team.
The engineers integrate Internet Of Things, Artificial Intelligence, Machine Learning, and Software Analytics with Spatial Network Graphs to gather all the relevant information and map it into a physics-based virtual simulating model and then by applying Analytics into these models, we get the performance characteristics of the physical asset. For most of the devices, the seamless exchange of data helps in getting the best possible analysis, the same is the case for digital twin. Therefore, a digital twin continuously updates itself from multiple sources to represent its near real-time status, working condition or position.
A digital twin also uses the data from past machine usage to factor into its digital model. The digital model created is then applied with analytics such as environmental conditions or interaction analytics with other devices to detect anomalies and the lifecycle of the physical counterpart. The twin then determines an optimal process that boosts some key performance metrics and provides forecasts for long-term planning which helps in optimising the business outcome.
The global market for digital twins is expected to grow very rapidly and talking in numbers, by almost 38 percent annually, it will be reaching $15.7 billion by 2023, according to MarketsandMarkets research.