‘Digital Twins’ is the new wonder-tech that has become crucial to businesses today. By replicating the physical assets of a product or service in an industry, digital twin helps in data analysis, lends a platform to examine the functioning beforehand to develop a solution for any potential problems. The term ‘Digital Twins’ came into existence when Michael Grieves, then computer engineer at the University of Michigan, wrote about it in 2002.
History
Simply, the digital twin is composed of three components:
- physical entities in the real world
- their virtual models and
- the connected data that tie the two worlds
NASA was the first to utilize the digital twin technology on a larger scale. The key objective was to devise a solution that can enable it to amend, update or check on a machine in outer space where physical presence is practically impossible at any given point in time. This inspired NASA to develop a virtual replica that would work from the desired place and can fetch real-time data pertaining to the actual model. Today, NASA uses digital twins, continuously, to come up with new recommendations, roadmaps, and next-generation vehicles and aircraft.
Digital Twins and other technologies
By using IoT sensors, digital twin collects and compiles information from a real-world counterpart following which it simulates the physical object in real-time, thereby offering insights into performance and possible issues. It is predicted that by 2022, over two-thirds of companies will be using digital twin technology for production purposes.
Companies are also infusing Big Data, Artificial Intelligence, Machine Learning, and software analytics to enhance the capabilities of the digital twin. Cloud connectivity facilitates a large-scale implementation of digital twin technology for companies in numerous industries. By blending these cognitive technologies and computing in the testing phase, digital twins can determine which product tests must run more frequently and which needs to be retired.
Advantages
· allows engineers to have access to a detailed, intricate view of a physical asset that is far away
· enables engineers to foresee maintenance failures through a recreation of models that catch data about different risk factors
· helps companies in developing new products as a service business model and in driving innovations in manufacturing, R&D, etc.
Some of the major players in the digital twin sector are Oracle, General Electric, Microsoft, PTC, ANSYS, Siemens, IBM, and Dassault System.
Digital Twin and its Applications
Manufacturing companies are using digital twins to augment industrial processes and offer better approaches to decrease costs, monitor assets, streamline maintenance, diminish downtime, etc. It also helps to model how product customization will affect the production process and hence facilitate a change in how the process works to accommodate this customization.
Companies are using digital twins for predictive maintenance by identifying deviations and anomalies in company operations. Digital twins also help developers to see how customers interact and experience the product and/or service a company provides for them accurately and hence troubleshoot any identified bottlenecks.
With the number of ongoing developments in the field that are connected to the emergence of the Internet of Things, Cloud computing, and Artificial Intelligence, the scope of digital twins is expected to see a steep rise in the future.
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