Technology has been rapidly advancing in functionality and complexity in the past few decades. This has led to increasing costs in its experimentation, updates are becoming increasingly risky and costly. While new designs could provide significant benefits to existing systems, organisations are weary of making changes without being sure of improved results. This is where digital modelling and simulation technology comes into picture. The latest such innovation is that of Digital Twin (DT) technology. DT has become a disruptive trend that will have a huge impact on the future of engineering.
The digital twin technology was first introduced in 2002 by Michael Grieves. DT is defined by the electronics conglomerate Siemens as: “A virtual representation of a physical product or process, used to understand and predict the physical counterpart’s performance characteristics. Digital twins are used throughout the product lifecycle to simulate, predict, and optimize the product and production system before investing in physical prototypes and assets.”
Research classifies digital twins into three sub-categories:
- Digital Model: A digital representation of an existing or planned physical object with no automated data exchange between the physical-digital objects.
- Digital Shadow: Exists as an automated one-way data flow between existing physical-digital objects.
- Digital Twin: In this the physical and digital objects are fully integrated, so data constantly flows between them.
Most of the currently popular interactive simulation tools fall under the Digital Model and Digital Shadow tool categories. Digital twins, however, combines a real-time simulation of system dynamics while managing facilities, systems and machines, while gathering data to drive performance. Using DT, engineers can optimize the product and/or production system before investing in physical models and changes.
DT provides data-driven representations of physical systems using Internet of Things (IoT) sensors and analytics. The three main components that make a digital twin system are a data model, a set of analytics or algorithms, and a set of executive controls.
Companies use sensors to monitor systems and model system dynamics and develop DT technology apt for their unique production. For example, a digital twin of an aircraft’s engine allows pilots to monitor the health of an engine in real time. General Electric has built digital twins of jet engine components to predict their remaining life and optimum maintenance intervals. Companies like Siemens are simulating and testing systems at the level of individual machines using DT. Entire factories can also be managed using DT technology. Top research and advisory company, Gartner, predicts that by 2021, at least half of the world’s large industrial companies will use digital twins and they will gain a 10% improvement in effectiveness from this shift. Digital twins are key in driving the industrial shift to automation.
Digital Twins and Engineering Education
Constant revision and development of course syllabi according to the changes and advancements in technology are important in engineering education. Indian colleges and professors must keep themselves and their facilities updated so that students are employment-ready when they graduate.
Using the latest technology to create and teach enables an experiential learning space for budding engineers. Enabling the use of digital twin technology among students will not only prepare them for the future workplace but also allow them to test industrial systems and machinery virtually and make changes in the physical world. Virtual learning is highly beneficial in saving costs, time and energy while improving physical machines and systems.
DT is a disruptive technology that is revolutionising the way industries operate. Engineering colleges must prepare to train their students in DT technology to stay current with the industrial changes that DT has brought.
An excellent way to teach DT technology and other rising innovations is to develop a virtual mechatronics lab for students. The use of digital tools and DT technology in engineering education will develop expertise and help improve students’ employability. Research also shows that learning with DT technology raises student motivation, enhances their learning, and increases students’ responsibility for learning.
However, as DT tools and software are quite complex and the learning curve will be high, colleges must ensure the availability and quality of necessary resources.