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Digital twins are virtual replicas of physical entities -object /machines/ bodies. With advanced technology, such replicas can be created as a per the real physical object’s construct at the micro atomic level and the macro geometric level as well. At it’s optimum standards, any information that can be obtained by inspecting the physical product, can be obtained from it’s digital Twin. Thus, a Digital Twin is a computer program which integrates IOT, AI, Machine learning and software analytics to create effective simulations of real life Object/Machine/body, that can predict how a product or process will perform, without using the physical object. While other simulations may help with understanding what can occur when changes are introduced, a digital twin adds value by helping to understand both what is happening currently and possible impacts of any change in condition or time, within a process. Advantage of this methodology is that study of a process involving any object/machine/body can be done remotely, as the virtual twin provides a contextual model of the object operations/working. Digital twins offer the following benefits to the user while developing a process or optimizing the same; - Transformative spatial awareness - Research without the risk of causing any “Real” damage - Practical observation of any defects, thus helping in RCA - Intelligent Recommendations become obvious easily - Corrective methods can be applied, as the digital twin allow the ability to self-tune and gives immediate insights needed for any preventive maintenance - Accelerated risk assessment and production time can be determined thereby optimizing the process & saving costs, reducing wastages and enhancing productivity - Real Time remote monitoring , helps to allow experts attend to any immediate requirement within short time as time to travel etc is saved. - Real Time, better team collaboration , which enables brainstorming and solution finding with as many number of people required, from wherever they are physically. - Financial decisions can be made immediately - Training can be done easily - Any control action or preventive maintenance can be determined without risking any damage while researching with the real object. The above mentioned advantages facilitate all the phases of the DMAIC process effectively and efficiently and thus contribute highly towards process development and optimization- They are indeed powerful masterminds to drive innovation and performance. Digital Twin is used in heavy Equipment and machinery work segment (Factory), in industries like Manufacturing, Engineering, Construction, Automotive- Digital Twin is aptly used in all phases like; - Design - Planning - Layout - Process Validation - Throughput optimization - Manufacturing / Construction execution - Scaling up production and Maintenance and sustenance of machines and devices Few other Industries where Digital Twin can be applied: - Education – Biology Students can actually understand the body parts of any animal, at any time without having to dissect one. Likewise, with students doing mechanical studies. I recently came to know that there is already a virtual and augmented reality laptop “ZSPACE”, a 3D laptop that is transforming learning. Students can be in the classroom. They need to use a pair of polarizing glasses, studded with tiny reflectors that transform the items on screen into digital twins in 3D and also track the user/student’s movement. The orientation of the 3D object will look correct from every angle. The mouse /stylo allows the student to pick up and move items, thereby fostering a fully immersive experience, with detailed simulations that a digital twin is supposed to. This allows students to explore human/animal bodies, car/aeroplane engines, or any physical entity that is seen on the screen- part by part in smallest detail. - Defence – Complete 3D Approach on interfaces and maps, improvement work on Weapon machines - Robotics- Developing humanoids, which can be used in defence industry as well - Infrastructure & Real Estate – developing underground structures such as metro lines and water pipelines – Smart Cities. - Telecommunication – Hardware enhancement - Healthcare- Simulation of medical equipment, chemicals, packaging etc. - Aeronautics and Space research- can be used for performance tuning- the spacecraft can be tuned from earth - Fashion – Modelling an outfit on the digital twin of a customer - Concept Designing in Marketing and Advertising- A 3D printer is a real boon - Customer Service – A customer service agent can guide a customer or technician remotely on correcting any defect in any appliance - Many Contact Centers use teh digital twin methodology. It must be remembered that the inception of this concept was for the purpose of Product Cycle Management (PLM) and is now adding value to many industries. Basically the concept can be used for any situation wherein help can be gained by replicating a physical entity.

Definition: A digital twin is a representation of a physical entity, operation, or service in digital form. A digital twin is a digital image of a physical entity, such as a jet engine or wind turbines, or even larger objects, such as houses or entire cities. The digital twin technology can be used to simulate processes in order to gather data and anticipate how they will work, in addition to physical properties. A digital twin is a computer programme that generates simulations based on real-world data to predict how a product or process would work. These virtual models have become a staple of modern engineering to drive innovation and boost performance, thanks to advances in machine learning and factors such as big data. In short, getting one will help develop strategic technology patterns, avoid costly failures in physical artefacts, and evaluate processes and services using advanced analytical, tracking, and predictive capabilities. How does it work? The creation of a mathematical model that simulates the original starts with experts in applied mathematics or data science studying the physics and operational data of a physical object or device. The developers of digital twins make certain that the virtual computer model will obtain input from sensors that collect data from the real-world version. This allows the digital version to replicate and simulate what is happening with the original version in real time, allowing for the collection of data on results and possible issues. A digital twin can be as complicated or as straightforward as you need it to be, with the amount of data used deciding how closely the model mimics the physical version in the real world. The twin can be used in combination with a prototype to provide input on the product while it is being created, or it can be used as a prototype in and of itself to simulate what could happen when the physical version is built. How to Design Digital Twins? As previously described, digital twins may be used for a number of purposes, including testing a prototype or design, evaluating and tracking lifecycles, and assessing how a product or process can perform under various conditions. Data is gathered and computational models are developed to test the digital twin architecture. This may involve a connection between the digital model and a physical model. This may include a real-time interface between the digital model and a physical object for sending and receiving input and data. 1. Data: In order to construct a virtual model that can represent the actions or states of a real-world item or system, a digital twin requires data about the entity or operation. This information may be relevant to a product's lifecycle and include design requirements, manufacturing processes, or engineering details. It may also provide information about the manufacturing process, such as machinery, products, and parts. It may also provide information about the manufacturing process, such as machinery, products, components, procedures, and quality control. Real-time input, historical review, and maintenance reports are examples of data that can be applied to operations. Company data or end-of-life procedures are examples of other data that can be used in digital twin design. 2. Modelling: If the data has been obtained, it can be used to construct predictive analytical models to display operating results, predict states like exhaustion, and predict behaviours. Tech simulations, physics, chemistry, statistics, machine learning, artificial intelligence, business theory, and goals can all be used to recommend behaviour. To aid human understanding of the results, these models can be viewed using 3D projections and virtual reality simulation. 3. Linking: Digital twin findings can be related to establish a summary, such as taking equipment twin findings and bringing them into a production line twin, which can then notify a factory-scale digital twin. It is possible to allow smart industrial applications for real-world operational advancements and improvements by using connected digital twins in this way. Application in Industries 1. Manufacture: Digital twins will increase efficiency and streamline processes while reducing throughput times. 2. Automotive: In the automotive industry, digital twins are used to capture and evaluate operational data from vehicles in order to determine their status in real time and inform product improvements. 3. Retail: Aside from manufacturing and business, digital twin is used in retail to model and optimise the consumer experience, whether at the level of a shopping centre or for individual stores. 4. Healthcare: Organ donation, surgical preparation, and operation de-risking have all benefited from digital twin in the medical field. Systems have also been built to model the movement of patients through hospitals and monitor where pathogens may occur and who may be at risk due to contact. 5. Disaster Management: Global climate change has had an effect on people all over the world in recent years, so using a digital twin to build smarter infrastructures, emergency response plans, and climate change monitoring will help to tackle it. Smart Cities: Cities will also benefit from the use of digital twins to become more economically, environmentally, and socially sustainable. Digital models can help planners make better decisions and provide solutions to the many complex issues that modern cities face. Real-time responses to problems, for example, can be guided by real-time data from digital twins, enabling assets such as hospitals to respond to a crisis in real time.