A Digital Twin: Origin, Types, Examples
A digital twin is a virtual representation that acts as the digital counterpart of a physical object or process in real-time. Though the concept was first proposed by Michael Grieves of the University of Michigan in 2002, the first practical definition of a digital twin was developed by NASA in 2010 in an effort to improve the physical-model simulation of spacecraft. Digital twins are the result of ongoing innovation in product design and engineering activities. Product drawings and engineering specifications have evolved from hand-drawn to computer-aided drafting/design to model-based systems engineering.
Mirror Worlds, David Gelernter’s 1991 book, predicted digital twins. Michael Grieves of the Florida Institute of Technology pioneered the use of digital twins in manufacturing. Grieves, then of the University of Michigan, publicly introduced the concept and model of the digital twin in 2002 at a Society of Manufacturing Engineers conference in Troy, Michigan. Grieves proposed the digital twin as the underlying conceptual model for product lifecycle management (PLM).
The concept, which had a few different names, was later dubbed the “digital twin” by NASA’s John Vickers in a 2010 Roadmap Report. The physical product, the digital/virtual product, and the connections between the two products comprise the digital twin concept. Data that flows from the physical product to the digital/virtual product and information that is available from the digital/virtual product to the physical environment are the connections between the physical product and the digital/virtual product.
Types of Digital Twin
Later, the concept was subdivided into types. The digital twin prototype (DTP), digital twin instance (DTI), and digital twin aggregate are the three types (DTA). The DTP is made up of the designs, analyses, and processes used to create a physical product. The DTP exists prior to the physical product. Once a product is manufactured, the DTI becomes the digital twin of each individual instance. The DTA is an aggregation of DTIs whose data and information can be used for physical product interrogation, prognostics, and learning. Use cases drive the specific information contained in digital twins. Because the digital twin is a logical construct, the actual data and information may reside in other applications.
Furthermore, the Digital Twin can be divided into three subcategories based on the degree of data and information flow that may occur between the physical part and the digital copy: Digital Model (DM), Digital Shadow (DS), and Digital Twin. A digital twin in the workplace is frequently regarded as a component of robotic process automation (RPA) and, according to industry analyst firm Gartner, is a component of the broader and emerging “hyper automation” category.
The use of 3D modeling to create digital companions for physical objects is an example of digital twins. It can be used to view the status of the actual physical object, allowing physical objects to be projected into the digital world. For example, when sensors collect data from a connected device, the data can be used to continuously update a “digital twin” copy of the device’s state. The concept of a digital twin is also referred to as a “device shadow.” The digital twin is intended to be a current and accurate representation of the physical object’s properties and states, such as shape, position, gesture, status, and motion.
A digital twin can also be used to optimize asset performance and utilization through monitoring, diagnostics, and prognostics. Sensory data can be combined with historical data, human expertise, and fleet and simulation learning to improve prognostics. As a result, complex prognostics and intelligent maintenance system platforms can use digital twins to solve problems and increase productivity.
Digital twins of autonomous vehicles and their sensor suite embedded in a traffic and environment simulation have also been proposed as a way to overcome the significant development, testing, and validation challenges for automotive applications, particularly when the related algorithms are based on artificial intelligence approaches that necessitate extensive training and validation data sets.
Dynamics Industry Level
The digital twin is causing havoc throughout the product lifecycle management (PLM) process, from design to manufacturing to service and operations. PLM is now very time-consuming in terms of efficiency, manufacturing, intelligence, service phases, and product sustainability. A digital twin can connect the physical and virtual worlds of a product. The digital twin allows businesses to create a digital footprint for all of their products, from design to development and throughout the product life cycle.
Digital twins are causing significant disruption in manufacturing industries. The digital twin serves as a virtual replica of near-real-time occurrences in the manufacturing process. Thousands of sensors are installed throughout the physical manufacturing process, collecting data from various dimensions such as environmental conditions, machine behavior, and work being performed. The digital twin communicates and collects all of this data in real-time.
Digital twins have become more affordable as a result of the Internet of Things, and they may drive the future of the manufacturing industry. Engineers benefit from the real-world application of products designed virtually by the digital twin. Because there is a digital twin of the real ‘thing’ with real-time capabilities, advanced methods of product and asset maintenance and management are now possible. By predicting the future rather than analyzing the past of the manufacturing process, digital twins offer significant business potential.
Digital twins’ representation of reality enables manufacturers to move toward ex-ante business practices. The future of manufacturing is driven by four factors: modularity, autonomy, connectivity, and digital twin. Opportunities for increased productivity are emerging as the stages of a manufacturing process become more digitalized. This begins with modularity and progresses to increased production system effectiveness.
Furthermore, autonomy allows the production system to respond to unanticipated events in a timely and intelligent manner. Finally, connectivity, such as the Internet of Things, allows the digitalization loop to be closed, allowing the subsequent cycle of product design and promotion to be optimized for higher performance. When products can detect a problem before it breaks down, customer satisfaction and loyalty may increase. Furthermore, as storage and computing costs fall, so will the number of applications for digital twins. Data integration, organizational, and compliance issues can all stymie the implementation of Digital Twins and their benefits.