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Applications of Digital Twins in Manufacturing

Erin Thompson |

As digital twin technology becomes increasingly prevalent, around 75% of companies in advanced industries are harnessing its capabilities. This spans from simulating production processes to generating replicas of equipment parts for performance analysis, providing organizations with a new approach to understanding their workflows. 

Digital twins streamline operations across many sectors, with manufacturing at the forefront. By offering real-time insights, digital twins play a pivotal role in Industry 4.0 and have become integral to smart factories and smart manufacturing. In this article, we will explore how this advanced technology is paving the way for enhanced productivity, predictive maintenance, and agile decision-making.

What is Digital Twin Technology?

A digital twin uses computer models, data, and analytics to create a virtual copy or mirror of a real-life object or system. By replicating it digitally, organizations are able to better understand, monitor, and predict its behavior in the real world. This allows them to gain insight into how it is functioning day-to-day.

Digital twins have the power to replicate not only small components of equipment, but also entire machine systems and entire workspaces. For example, an organization can make a virtual replica of an entire warehouse and use this digital twin to test different floor layouts, simulate workflows, inspect pipes and electrical systems, etc. As we will explore, this enables manufacturers to harness data-driven insights for predictive maintenance, process optimization, and quality enhancement. 

Mars, the food and pet care company, for instance, optimizes its supply chain using Microsoft’s Azure Digital Twins IoT service to augment operations across its 160 manufacturing facilities. As a result, the company has boosted uptime and has been able to detect and refine packaging inconsistencies of products. 

Digital Twin vs. Simulation

While a digital twin is a simulation of a physical product, system, or process, they are actually different from simulations themselves. Digital twins act as virtual counterparts of real-world machinery or processes, such as production equipment and the tasks it performs. Therefore, they are continuously updating themselves based on live data, mimicking real-world occurrences. 

In contrast to digital twins, simulations are more broad and involve creating models to mimic real-world systems. These virtual experiments are commonly used in industries like aviation and finance for training and risk analysis without the real-time link to the physical system. 

However, simulations lack the data integration present in digital twins, making simulations less adept at responding to real-time changes and therefore are better for complex modeling and hypothesis testing. 

How Does Digital Twin Technology Work?

Types of Digital Twins

1. Component Twins

Component twins are the lowest level of digital twin technology that focus on individual parts or the smallest components found within a larger asset. They give organizations detailed insights into how parts, such as sensors, switches, or bolts, work and interact in the bigger picture. By replicating individual components, component twins can provide a full health analysis on every aspect of an asset. 

For example, in the automotive industry, components of an engine can be transformed into digital replicas. Companies can then use these digital twins to predict when parts might fail, plan maintenance ahead of time, and make improvements for improved performance. 

2. Asset Twins

Asset twins often consist of several component twins and they focus on replicating complex physical assets such as machinery, equipment, or infrastructure. They analyze how efficiently individual components interact together and perform as a whole. 

We can consider a power plant, where generators, turbines, and control systems can be digitally replicated. These twins then provide manufacturers with valuable insights into the performance, condition, and utilization of these assets in real-time. This allows them to monitor asset health, predict potential failures, optimize maintenance schedules, and even simulate scenarios for better decision-making. 

3. System Twins

System twins duplicate assets on a systems level. They are tailored to replicate entire manufacturing systems or production lines and therefore give a large-scale overview of a factory or plant. By leveraging system twins, companies can monitor and analyze their internal processes within their manufacturing facilities in real-time. For instance, in aircraft manufacturing, system twins can be employed to replicate an aircraft's fuel system, providing insight into the system’s performance and potential safety risks.

4. Process Twins

Finally, process twins combine system twins into a single entity, showing how different systems work together and enabling a more thorough analysis of results. By virtually replicating or simulating a real-world manufacturing or operational process, organizations can continuously test, forecast, and optimize it in live conditions. 

Consider a process twin of a manufacturing plant, which may include the simulation of an entire production process, from raw materials to a finished product. This technology enables the organization to identify inefficiencies, improve quality control, and enhance overall productivity. 

Applications of Digital Twins in Manufacturing

Factory Design and Operations Planning

product and components - optimized  (1)

By creating virtual replicas of factory floors, equipment, and processes, manufacturers can visualize and optimize various layouts, workflows, and operational scenarios before implementing changes into the physical environment. For example, Siemens Numerical Control in China produces production systems, drives, and motors. To cut down on facility costs, they have consolidated 3 factories into 1 using digital twins, allowing them to better optimize space and reduce waste being produced from these facilities.

Additionally, digital twins play a crucial role in Manufacturing Operations Management (MOM), a system that oversees aspects like quality and performance monitoring in operations. Digital twins can also integrate with Factory MES (Manufacturing Execution Systems), computerized systems monitoring shop floor operations. Integrated seamlessly, they allow precise and efficient monitoring and control of manufacturing operations.

Digital Twins of Products and Components

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Digital twins also play a crucial role in product and component development by creating digital replicas for continuous monitoring and analysis. This allows manufacturers to scrutinize real-time behavior and performance of individual items, such as an engine or robotic arms, capturing detailed information about their design, behavior, and performance. 

In fact, General Electric uses digital twins for aircraft engines in order to monitor the maintenance of the engine blade and understand when it might need to be repaired. By leveraging digital twin software, companies like General Electric can analyze how different design variations or manufacturing processes might impact their product performance and quality.

Maintenance and Performance Monitoring

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As we have explored, digital twins enable manufacturers to implement preventative maintenance strategies more effectively by analyzing real-time data from sensors. This allows organizations to identify potential issues before they lead to downtime, track equipment health and Overall Equipment Effectiveness (OEE) to make efficiency improvements.

One example may be using digital twins to replicate a conveyor system. In doing so, an  organization can analyze the system's performance to better predict when it might need maintenance. Plus, when they are integrated with Manufacturing Execution Systems (MES), digital twins have the power to enhance reliability of operations, minimize downtime, and contribute to long-term cost savings.

Employee Training

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Companies can additionally use digital twins to provide immersive and interactive training experiences for workers with realistic simulations of workplace scenarios. Employees can learn by virtually operating machinery, troubleshooting issues, and practicing safety protocols in a risk-free environment. 

For example, Siemens has also created a digital twin of its electric motor production processes and are using it to train their employees. They are able to learn the assembly and troubleshooting processes without altering the physical production process. This hands-on, scenario-based training approach has been seen to enhance retention and allows workers to better understand tasks. 

Digital Twins of Automated Robots

AR twins - optimized (1)

Lastly, digital twins can provide virtual replicas that mirror the behavior and functions of automated robots. These digital counterparts are continuously updated with real-time data from sensors on the actual robots, allowing manufacturers to monitor performance to optimize efficiency, predict maintenance needs, and prevent potential breakdowns. 

For instance, Cyngn created a simulation of a warehouse environment that used the UnReal Physics engine, which created a twin of the environment. We tested our 3 vehicles in this simulation to see how well their safety features performed and found that each vehicle could safely stop and change course in the presence of obstacles. By leveraging digital twins, operators can simulate and analyze different scenarios like this, allowing for precise programming and refining of robotic tasks.  

Digital Twins For Automated Guided Vehicle Management

Just as digital twins can be developed for automated robots, organizations can create virtual replicas of automated guided vehicles (AGVs) and their operational environment. Using this technology, their digital counterparts are constantly being updated with real-time data from sensors to monitor where they are, their performance, and if they require maintenance. This allows them to simulate and assess different scenarios and refine algorithms. 

An example is the Düsseldorf Airport in Germany, which has deployed AGVs to assist in baggage handling. By creating a digital twin of that environment and simulating the AGVs’ tasks, they found that the number of AGVs needed could be reduced by 13%, saving costs.

7 Benefits of Digital Twins for Manufacturing

1. Improved Workflows and Processes

According to The World Economic Forum, leveraging digital twin technology can increase operational efficiency by 10%. This is reflected in the global aerospace and defense company, Rolls-Royce, which uses digital twins of its engines to monitor maintenance needs. By doing this, they have reduced its carbon output by an impressive 22 million tons.

Additionally, digital twins play a crucial role in smart manufacturing initiatives. Through integration with advanced technologies such as IoT and AI, they create interconnected and intelligent production systems, enabling real-time data exchange for adaptive decision-making and continuous optimization.

2. Preventative Maintenance

As we have explored, digital twins continuously analyze real-time data from physical machinery and equipment to provide insights into overall equipment effectiveness (OEE). This helps manufacturers identify areas for improvement and detect potential issues before they occur in the actual production environment, such as worn out or broken parts, and identifying places for equipment malfunctions. 

3. Better Designs

Digital simulations created by digital twins enable engineers to experiment with different design variations and assess performance under various conditions. Take IBM, which uses digital twins to virtually design products and test different designs, providing enhanced insights into the most optimal designs before physical production actually begins.

Additionally, digital twins can identify potential issues early in the product development phase, including structural weaknesses, functional inconsistencies, or compatibility issues. According to a survey by Altair, 94% of participants in manufacturing organizations noted that digital twins have enhanced their decision-making in the development of new products. Through utilizing digital twin technology, manufacturers can showcase design concepts, identify flaws, and solicit feedback to refine their designs early on. 

4. Lower Costs

Digital twins contribute to cost reduction by optimizing various aspects of operations and enabling manufacturers to make more informed, data-driven decisions. This includes optimizing resource utilization, reducing maintenance costs, and minimizing downtime. As a result, organizations can boost operational efficiency, leading to resource savings and decreased operational costs. 

Consequently, digital twins have recently been shown to cut product development times from 20-50% for some users. In fact, on average, manufacturing companies that have implemented digital twins into their workflows have seen a 13% reduction in maintenance costs and a 15% increase in efficiency.

5. More Resilient Supply Chains

In the era of smart manufacturing and Industry 4.0, digital twins play a crucial role in creating agile and responsive supply chains that can quickly adjust to changing market conditions. Manufacturers can gain greater visibility by creating digital replicas of their entire supply chain, including suppliers, production processes, and distribution networks. This enables real-time monitoring of the supply chain's performance, allowing them to identify vulnerabilities and potential disruptions early on. 

6. Continuous Improvement

By continuously monitoring and analyzing real-time data, companies can uncover hidden patterns and optimize performance. The dynamic nature allows for ongoing simulations, scenario testing, and strategizing.

7. Life Cycle Analysis

Digital twins perform lifecycle analysis by collecting and integrating data across the entire lifespan of a product, asset, or system. This includes data related to design, maintenance, disposal/recycling phases, etc. By aggregating this data, digital twins can identify opportunities to optimize and implement sustainable practices of an asset throughout its life cycle. 

How Digital Twins Can Be Used for Autonomous Industrial Vehicles in Manufacturing

In the same way that digital twins can replicate automated robots and AGVs, they can also replicate autonomous industrial vehicles (AIVs) in manufacturing. By creating virtual replicas for real-time monitoring and analysis, digital twins can contribute to enhanced efficiency and operational reliability in autonomous vehicle systems by:

1. Creating a virtual version of vehicles for testing.
2. Improving algorithms for autonomous control by updating with real data.
3. Predicting and fixing problems in autonomous vehicles, reducing downtime.
4. Optimizing operations to increase productivity by planning the most optimal routes and workflows.
5. Utilizing real-time data to make decisions that improve safety and performance.

Additionally, digital twins are often integrated with the Industrial Internet of Things (IIoT), allowing for seamless communication between AIVs and other connected devices. As a result, this integration creates the coordination and optimization of tasks across the factory floor.

Frequently Asked Questions

1. What is digital twin technology?

Digital twin technology involves creating virtual replicas of physical objects, processes, or systems. These virtual representations are generated using real-time data and mirror their real-world counterparts. They are used for real-time monitoring, analysis, and optimization, providing valuable insights across various industries such as manufacturing, healthcare, and infrastructure.

2. What are some examples of digital twins?

Examples of digital twins span various industries. For instance, in manufacturing, a digital twin may replicate a production line to optimize efficiency and identify potential issues. In healthcare, digital twins can simulate physiological processes for personalized medicine, and in smart cities, they may model urban infrastructure for better management and planning. Digital twins have a variety of applications that can benefit many different sectors.

For organizations to best utilize digital twin technology, it’s important to consider the types of digital twins available that would be the most valuable. More broadly, digital twins include component, asset, system, and process twins which all have unique applications.

3. How can digital twins be used in factories?

In factories, digital twins streamline operations by enabling predictive maintenance, optimizing production processes, and improving quality control. They also contribute to agile supply chain management and support workforce training, making factories more efficient, resilient, and sustainable. 

4. Do digital twins use AI?

Yes, digital twins often incorporate artificial intelligence (AI) algorithms for data analysis, predictive modeling, and decision-making. AI enhances digital twins by enabling them to process large volumes of data, identify patterns, and make real-time adjustments based on changing conditions. 

5. What is a digital twin warehouse?

A digital twin warehouse is a virtual replica of a physical warehouse facility. This representation simulates the layout, inventory, equipment, and operations of the actual warehouse in a digital environment. Companies use digital twin warehouses for various purposes, such as optimizing layout design, simulating storage configurations, and planning workflows. 

6. What industries use digital twin technology?

Digital twin technology is used in many diverse industries, including manufacturing for optimizing production, healthcare for personalized medicine, and smart cities for infrastructure planning. Additionally, the aerospace industry utilizes digital twins for predictive maintenance of aircraft systems, while the automotive industry employs digital twins for vehicle design and testing. As a result, digital twins offer benefits for a wide range of organizations, regardless of their application.

7. What’s the difference between digital twins and simulations?

The difference between digital twins and simulations lies in their scope and purpose. While simulations replicate specific scenarios or processes, digital twins replicate entire systems or objects in real-time. For example, machine parts can be created digitally using a digital twin while simulations virtually represent the way that machine is running in day to day operations. 

Simulations are often used for predictive analysis or testing specific hypotheses, whereas digital twins provide ongoing monitoring, analysis, and optimization of physical assets or systems. In comparison, digital twins continuously update with real-world data, enabling predictive maintenance and decision-making based on current conditions.

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