Digital Twin Technology and Its Impact on Manufacturing and Business
With the rapid shift towards the use of cyber-physical systems in industries (aka Industry 4.0), digital technologies now play crucial roles in several applications.
One of such technologies– known as digital twins is currently disrupting the manufacturing and business sectors.
What are Digital Twins?
A digital twin is a dynamic digital replica of an object, structure, or system. Digital twins can be used to model simple and complicated or small and large structures–ranging from automotive components to buildings, cities, and even humans. In a broad sense, we can think of digital twins as an intersection between the physical and virtual worlds.
The earliest use of digital twins was by NASA, during the unsuccessful Apollo 13 mission, for creating virtual replicas of its space machines that earth stations monitored remotely. However, the term became popularized after Gartner recognized as a top strategic technology trend for 2017.
A digital twin of an aircraft designed by Siemens. Image used courtesy of Siemens.
How are Digital Twins Created?
Digital twins are built by a team of engineers, product designers, and data scientists. The engineers and designers work together to design the physical prototype of an object to the required build, materials, and dimensions using computer-aided design (CAD) software. Using the knowledge of the underlying physics of the object, data scientists extract data to generate a mathematical model for the virtual world.
The digital replica must be identical to the physical product in every aspect to model its behavior and characteristics accurately. Organizations that require digital twins will either create the models in-house or outsource it to a separate company–after specifying their product requirements.
After carrying out extensive testing and simulation, the digital twin is delivered to the client for use. Prominent vendors in the digital twin space include Bosch, IBM, and Siemens.
How Digital Twins Work
Digital twins are designed to receive input signals from sensors attached to the physical object and produce real-time output or feedback that describes how the object would behave in a virtual environment. Manufacturers can use this information to run simulations and/or refine prototypes before deploying the final product.
The two main technologies behind digital twins is the internet of things (aka IoT) and machine learning, a subset of artificial intelligence (AI). The IoT comprises smart, connected devices that interact and exchange information over networks. Machine learning, on the other hand, refers to statistical models and algorithms that enable computer systems to carry out specific tasks without acting on any explicit instructions.
The basis of interactions of these technologies is to generate data. The data allows developers to continuously optimize the virtual replica to allow it to adapt to changes to its real-world twin for days, months, or even years.
A digital twin of a turbine by General Electric. Image used courtesy of General Electric.
What Are Some Applications of Digital Twins?
Digital twins are beneficial to several applications in manufacturing and business:
One of the most prominent applications of digital twins is in manufacturing, where it can help to address quality and performance issues in physical products. Using augmented reality (AR) visors, engineers can see a machine in operation in the virtual world, allowing them to identify defects, adjust the orientation of parts, and predict the end of life of the product.
For example, Black & Decker–an industrial tools manufacturer–uses digital twin technology to create virtual mockups of entire assembly lines and parts of its factories, allowing to carry out production more efficiently. General Electric also utilizes digital-twin technology in some of its business operations.
The company creates virtual models of jet engine components to predict when certain parts would be due for maintenance or replacement. Using digital twins in this way can improve the reliability of existing products and reduce maintenance costs and time to market (TTM) for new products.
Digital twins are valuable to companies in several ways. Firstly, applying AI and data analytics to virtual replicas of business processes enables data-driven decision making that gives management valuable insight into the strengths and weaknesses of its operations. Secondly, a digital twin allows research and development (R&D) teams to study products in real-time and apply modifications where necessary, saving huge prototyping costs.
Lastly, using digital twins can enhance business logistics. A report by DHL explains how digital twins can be used to create virtual models of entire supply chain networks. A key application highlighted in the report is container fleet management. IoT sensors embedded into shipping containers can help to inventory and monitor the physical conditions of storage. Data is collected in real-time by the digital twin of the container network.
Can Digital Twins Be Integrated?
According to Gartner’s research, it is possible for more multiple digital twins to overlap to perform a unified function, although this can be a complex process. For example, composite data twins at factories could integrate discrete digital twins of individual pieces of equipment.
According to a Gartner survey, 75% of companies implementing IoT are already using digital twins in their operations or planning to do so by 2020. Evidently having a profound impact on manufacturing and business, we can expect this trend to continue well into another decade.