Applications of Digital Twin Technology in Manufacturing and Beyond
Digital twins, virtual copies of physical entities or processes, are transforming manufacturing and being applied in a wide range of domains. In manufacturing, they are improving product design, streamlining production processes, facilitating predictive maintenance, and enhancing overall operational effectiveness. Beyond manufacturing, Digital Twin technology is being used in healthcare for personalised treatment and surgical planning, in smart cities for optimising infrastructure and traffic patterns, and in disaster management for simulating and preparing for different scenarios.
In this blog post, we will explore the applications of Digital Twin Technology in manufacturing and beyond, including its types and benefits. So, let’s dive in!
Table Of Content
What is Digital Twin Technology?
Types of Digital Twin Technology
Applications of Digital Twins in Manufacturing and Beyond
Key Benefits of Digital Twin Technology
The Bottom Line
Frequently Asked Questions
What is Digital Twin Technology?
A Digital Twin is a virtual copy of a physical object or system in the digital world, developed by combining data from different sources to replicate its physical equivalent. The digital model can be monitored in real time, simulated, and analysed to reflect the physical asset, enabling users to understand its behaviour, detect problems, and make informed decisions about its usage and maintenance.
Digital Twin Technology is a process that predicts the performance of a product or process based on actual-world data. Such applications use the Internet of Things (IoT), artificial intelligence (AI), and data analytics to enhance outcomes. The digital twin also contains support data such as the firmware version, configuration, calibration, and setpoint data of the device.
Types of Digital Twin Technology
Different types of Digital Twin Technology include:
Component/Part Twins
These are the most elementary level, dealing with individual parts or components of a system. For instance, a digital twin of a sensor or a motor.
Asset Twins
This twin type combines several component twins to describe a physical asset, such as a vehicle or a machine. It enables analysis of how the components interact and work together.
System/Unit Twins
System twins simulate the interaction of several asset twins in a complex system. This system level gives insights into the system’s overall performance and behaviour.
Process Twins
These are the most detailed twins, encompassing the whole process or value stream of a product or system. They can optimise workflow, forecast results, and enhance overall efficiency.
Digital Twin Prototype (DTP)
Developed prior to a physical product, DTPs are employed for simulation and design purposes.
Digital Twin Instance (DTI)
DTIs are created when the physical product is present and utilised for real-time monitoring, testing, and analysis.
Digital Twin Aggregate (DTA)
DTAs use data gathered from the physical product to forecast the future behaviour and performance of the product.
Applications of Digital Twins in Manufacturing and Beyond
Now that you understand ‘what is Digital Twin Technology’ and its types, let’s explore its applications in manufacturing and beyond.
Digital Twins in Manufacturing
Product Design and Development
Product design and development with Digital Twin Technology enables manufacturers to develop virtual twins of physical products, which can be simulated, tested, and optimised prior to the actual creation of physical prototypes. This saves time to market, decreases development expense, and enhances product quality by detecting issues at early stages of design.
Process Optimisation
Digital twins in manufacturing allow for optimisation of processes via simulation, real-time monitoring, and predictive maintenance. Companies create a virtual representation of a manufacturing process and can simulate various scenarios, detect bottlenecks, and make efficiency improvements without affecting physical operations. This results in lower downtime, better allocation of resources, and higher overall productivity.
Quality Management
Digital twins transform manufacturing quality management through real-time monitoring, predictive analytics, and process optimisation. This enables manufacturers to detect quality issues at an early stage, minimise defects, and enhance overall product quality and consistency.
Predictive Maintenance
Digital Twin Technology improves predictive maintenance in the manufacturing sector through virtual models of physical assets that enable real-time tracking, failure forecasting, and optimal maintenance plans. This results in lower downtime, better asset life expectancy, and lower maintenance expenses.
Operational Efficiency
Digital twins in manufacturing improve business efficiency through real-time monitoring, predictive maintenance, and optimised production processes. Digital twins allow manufacturers to detect bottlenecks, eliminate downtime, and optimise equipment effectiveness (OEE). Through various scenarios, digital twins optimise resource planning, reduce waste, and improve time to market.
Training and Simulation
Digital twins in manufacturing allow for full-scale training and simulation, where real-world practice is simulated on sophisticated equipment, process improvement, and risk avoidance before production takes place. Safety is increased, operator competence is improved, and the earlier detection of issues results in more productive and safer manufacturing.
Digital Twins Beyond Manufacturing
Supply Chain Management
Digital twins transform supply chain management by creating virtual representations of real-world supply chains, facilitating real-time tracking, predictive analytics, and the simulation of different scenarios. With this technology, companies can maximise inventory, reduce transportation costs, minimise risks, and generally improve supply chain performance.
Healthcare
Digital twins in healthcare have diverse applications ranging from personal medicine and surgery planning to optimising hospital operations. They provide real-time monitoring and proactive intervention to improve patient care and enable medical research and drug discovery.
Smart Cities
Digital twins, virtual copies of physical objects or processes, are revolutionising both manufacturing and smart cities by allowing real-time monitoring, simulation, and optimisation. In manufacturing, they enhance efficiency and product quality via predictive maintenance and optimised production. In smart cities, they optimise urban planning, infrastructure management, and resource allocation.
Disaster Management
Digital twins are increasingly applied in disaster management, particularly for manufacturing and other complex systems, to model scenarios, advance response times, and increase overall resilience. Through the creation of a digital version of a physical system, digital twins enable the simulation of disaster situations, such as earthquakes, floods, or factory accidents, to analyse possible effects and create sound mitigation strategies.
Energy
Manufacturing digital twins, especially in the energy industry, provides advantages in achieving maximum energy efficiency and sustainability. Through the development of virtual models of physical assets and processes, digital twins allow real-time monitoring, preventive maintenance, and resource optimisation, resulting in less energy use and enhanced operational performance.
Key Benefits of Digital Twin Technology
Here are some key benefits of Digital Twin Technology.
Increased Efficiency
Digital twins in manufacturing increase efficiency through real-time monitoring, predictive maintenance, and optimised procedures. This results in decreased downtime, lower maintenance expenditure, and increased overall equipment effectiveness.
Reduced Costs
Digital Twin Technology decreases the cost of manufacturing through optimised processes, predictive maintenance, and waste reduction. This leads to decreased operational costs, lower downtime, and enhanced product quality.
Better Decision-making
Digital twins in production enhance decision-making through offering real-time insights and the ability to test different scenarios before making changes to the physical environment. This helps manufacturers make better decisions, streamline processes, and reduce risks, eventually leading to greater efficiency and productivity.
Increased Collaboration
Digital twins in industry boost collaboration by allowing a common, virtual platform for teams to collaborate on product design, production process, and maintenance. Real-time access to data and analysis within this common environment facilitates smooth communication and coordination across departments, such as engineering, design, production, and maintenance.
Risk Mitigation
Digital twins in manufacturing provide immense risk mitigation advantages. They enable companies to experiment and verify products, processes, and situations virtually prior to actual production, detecting pitfalls and failures. This anticipatory strategy minimises the risk of expensive errors, rework, and downtime in the real-time of production.
The Bottom Line
Digital Twin Technology provides significant potential for revolutionising manufacturing by creating virtual representations of physical assets, processes, and systems. By utilising digital twins, manufacturers can enhance efficiency, minimise costs, improve product quality, and achieve a competitive edge. Starting with a specific use case and then scaling up is a recommended approach for the successful adoption of Digital Twin Technology. This approach helps manage complications, improve processes, and determine value before expanding to more extensive applications.
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Frequently Asked Questions
AWS digital twin, in the form of AWS IoT TwinMaker, is an AWS service that makes it easier to create and utilise digital twins for physical systems. It enables users to build virtual models of physical assets, such as buildings, factories, or equipment, and link them to real-world data streams. This allows users to track, analyse, and optimise operations based on what the digital twin does and how it performs.
Digital Twin Artificial Intelligence is the combination of Artificial Intelligence (AI) methods with Digital Twin technology. Digital twins are virtual models of physical objects or systems, and AI augments them by adding advanced capabilities such as predictive analytics, automated decision-making, and optimised operations.
Digital twins leverage different technologies such as IoT, AI, and data analytics to sense data from the physical world and construct a virtual representation. This virtual model can then be utilised to predict various scenarios, examine performance, and optimise operations without directly dealing with the physical entity.
Initiation with digital twins requires understanding your unique needs and requirements, determining relevant use cases, and choosing suitable technologies and platforms. A pilot project is the best place to begin, gaining hands-on experience and developing internal capabilities before expanding.
Digital twins are based on a group of technologies, with the Internet of Things (IoT) being the major one, supported by Artificial Intelligence (AI), Machine Learning (ML), data analysis, and visualisation software. They combine all these to form a virtual copy of a physical object or system that is constantly updated with real-time information to reflect its behaviour and performance.
