Having pros and cons, Digital Twin technology in engineering education is improving the learning process
Digital Twin technology is a critical component of Industry 4.0. Digital Twin technology in engineering education, is essential to keep the curricula updated. Students, teachers, and organizations are provided up-to-date information by this Digital Twin technology in engineering education. Digital Twin tools such as Digital Shadow can help them to get the information. This may have an influence on student employment as well as company competitiveness. This technology should be adopted by the total education system, though it has several advantages and disadvantages. Digital Twin technology in engineering education has evolved into a disruptive trend that will have a significant influence on engineering’s future.
Michael Grieves was the first to introduce Digital Twin technology in 2002. It is a digital depiction of a physical thing or process that’s updated in real-time. The production of Digital Twins is the outcome of continuous progress in product design and engineering operations. From manual drafting through computer-aided drafting or computer-aided design to model-based systems engineering, product drawings and engineering requirements have advanced.
Cost savings, faster time-to-market, and predictive maintenance are all advantages of Digital Twin technology. It can be classified into three categories: Digital Model, Digital Shadow and Digital Twin.
Digital Model: It is a digital representation of a physical thing that exists or will exist, but there is no automatic data interchange between the real and digital objects.
Digital Shadow: It occurs as a one-way automatic data flow between physical and digital things.
Digital Twin: In it, data travels between physical-digital items that are fully linked in both directions in a Digital Twin.
The Digital Model and Digital Shadow tool categories encompass the majority of today’s most popular interactive simulation technologies. Digital Twin, on the other hand, integrate a real-time simulation of system dynamics with the facility, system, and machine management, as well as data collection to drive performance. Digital Twin technology can be used by engineers to optimize a product or a manufacturing system before investing in physical models and modifications.
Digital Twin technology uses Internet of Things (IoT) sensors and analytics to create data-driven representations of physical systems. A data model, a collection of analytics or algorithms, and a set of executive controls are the three primary components that go into a Digital Twin system.
In engineering education is critical to constantly revise and adapt course curricula in response to changes and developments in technology. Allowing students to use Digital Twin technology will not only prepare them for the future job, but it will also allow them to digitally test industrial systems and machinery while making modifications in the real world. Using cutting-edge technology to develop and teach allows young engineers to engage in hands-on learning. Colleges must assure the availability and quality of required resources because DT tools and software are highly sophisticated and the learning curve will be steep.
Digital Twin technology is a game-changing technology that is changing the way companies’ function. To keep up with the industrial developments that Digital Twin has brought, engineering institutions must prepare to teach their students in Digital Twin technology. Creating a virtual mechatronics lab for students is a fantastic method to teach Digital Twin technology and other emerging technologies. In engineering education, the utilization of digital tools and Digital Twin technology will help students gain experience and increase their employability. Learning using Digital Twin technology also boosts student motivation, improves learning, and increases student accountability for learning, according to research.
Digital Twin is expected to be used by at least half of the world’s biggest industrial businesses by 2021, resulting in a 10% increase in inefficiency. It is crucial in accelerating the industrial automation transition.