Digital Twin

Introduction to Digital Twin

Just a decade or two ago, physical space played main role in industry. Human beings organized physical assets, close in distance, to handle design and manufacturing tasks. However, due to limited personal capabilities and geographical constraints, high efficiency could not be achieved, especially in product development. The rapid development of new technologies like Internet of Things (IoT), Big data analysis, augmented reality (AR), artificial intelligence and cloud computing is changing all this. Collectively called as Industry 4.0, it has ushered exciting changes in the way things are manufactured. Europe and America are the largest manufacturers of machine tools, especially high value parts for such competitive sectors like aerospace and automotive. India and China are fast catching up, and the race for who dominates the manufacturing sector in the coming years will now depend on who adopts to better technology faster. In order to gain a competitive edge, the manufacturing industry has to embrace the tenets of industry 4.0 and technological advances such as cyber-physical systems. The cyber twin, better known as the Digital twin, is becoming an integral part of this development. A digital twin is a focused application of the cyber-physical system and provides more practical values and implementation details, and therefore provides a pragmatic way for seamless integration and fusion. Before we discuss the merits and demerits of digital twins in the context of IIoT, let us first understand what exactly the term means.  

Defining a Digital Twin  

A digital twinis virtual replica of a physical product, process or a system. It is a digital representation or a digital instantiation that includes the virtual model of the physical object, data from the object, a unique one-to-one correspondence to the object and the ability to monitor the object. This pairing of the virtual and physical worlds allows analysis of data and monitoring of systems to mitigate problems before they even occur, prevent downtime, develop new opportunities, and even plan by using simulations. Simply put, a digital twin is a virtual representation of what has been manufactured in the real world. A digital twin thus acts as a bridge between physical and digital worlds. It does so by using sensors to collect real-time data about a physical item. One of the first users of digital twins was NASA, which has been using this concept successfully to emulate space flights on the ground.  

The Working of Digital Twins  

To create a digital twin, the engineers collect and collate all kinds of data. This includes physical, manufacturing and operational data, which is then synthesized with the help of Artificial Intelligence (AI) algorithms and analytics software. The end result is a virtual mirror model for each physical model that has the capability of analyzing, evaluating, optimizing and predicting. It is important to constantly maintain synchronous state between the physical asset and the digital twin, so that the consistent flow of data helps engineers analyze and monitor it. The basic architecture of digital twin consists of the various sensors and measurement technologies, IoT and AI. From a computational perspective, the key technology to propel a digital twin is integration of data and information that facilitates the flow of information from raw sensory data to high-level understanding and insights. Digital twins comprise four basic elements:  

  • the model that mirrors a real world system
  • IoTsensors that transmit data in real time
  • Data that helps synchronize the model and the system
  • software (like PTC ThingWorx / Vuforia)that monitors and analyses data in a meaningful way

The key functionality of digital twin implementation through physics based models and data driven analytics is to provide accurate operational pictures of the assets. The Industrial IoT system carries out real-time data acquisition through its smart gateway and edge computing devices. The digital twin thus combines modelling and analytics techniques to create a model of specific target.  

Steps involved in creating a digital twin:  

  • Physical to digital: Engineers capture all sorts of data from the physical asset with the help of various sensors and convert it into digital record
  • Digital to digital: use advanced analytics, scenario analysis and Artificial Intelligence (AI) to gain and share meaningful information
  • Digital to physical: apply algorithms to translate digital world decisions to effective data in order to spur action and change in the physical world 

A few popular software tools used in India and abroad for this purpose are PTC ThingWorx, for Industrial IoT and PTC Vuforia for Augmented Reality.  

With the converging of digital technologies like AR, VR and AI in IIoT, digital twins are gaining importance, especially in the context of Industry 4.0. They are drivers of innovation and performance, providing technicians with the most advanced monitoring, analytical and predictive capabilities.  

Advantages of Digital Twins  

- Performance optimization: Digital twin helps to determine the optimal set of parameters and actions that can help maximize some of the key performance metrics and improve forecasting accuracy

- Prediction: using different modelling techniques and algorithms, the digital twin model can be used to predict the future state of the machines. For example, a digital twin can predict when race car tyres will wear out on the track and suggest immediate replacement

- Maintenance and analysis: accurate data analysis (when the right software is used) helps the physical counterpart perform better.  

As can be seen, digital twins determine the best course of action by eliminating the guesswork so service the critical assets in the manufacturing units. The increasing adoption of IoT is ideal for enterprises to leverage digital twin platforms to boost their services and platforms.  

Scope for Digital Twin  

The potential of digital twin is truly phenomenal. As the concept and the technology evolve, the collaborative and predictive impact of digital twins cannot be undermined. As an example, take the case of industrial chemical reactors. Let's say a company has erected a chemical reactor and also created its digital twin. Let’s say that the reactor is designed to implement a specific chemical reaction. However, due to operator negligence, the proportion of chemicals that need to be mixed has been incorrect. The digital twin’s control system notices some unusual sensor input that it interprets as crossing the danger threshold. As a precautionary measure, it immediately shuts down the reactor and alerts the crew. Depending on the severity of the situation, the crew then takes further action. Not only does the digital twin save the company money, but if the chemicals are dangerous, it also prevents a possible explosion and saves precious lives.  

Sectors that benefit from digital twins include aerospace, defence, automotive, heavy machinery, consumer goods & electronics, power & energy etc. And while the concept of digital twins primarily focuses on manufacturing, it is finding use cases in areas as diverse as healthcare, supply-chain and even smart cities.

As per estimates, the market for digital twin is set to grow at a rate of more than 30% per year from 2020 till 2025. An increase in demand for IoT and cloud-based platforms like ThingWorx and a desire to be globally competitive are set to propel the demand for digital twin technology. India and other countries are already investing into IIoT; digital twin is one more step in the right direction.