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As the adoption of the Internet of Things (IoT) in industrial settings has increased in manufacturing environments over the years alongside smart manufacturing systems, businesses have increasingly looked to leverage these tools to enhance the way they operate as well as improve productivity and efficiency.
One trend that has been on the rise in recent years is the use of digital twins to connect physical objects in a manufacturing environment with digital systems to help augment and improve the way products are developed. According to Deloitte, investment in digital twin technology is projected to grow to $16 Billion by 2023.
In this post, we’ll be exploring the history of digital twins, how they’ve been adopted in manufacturing industries, and the benefits they provide businesses as it relates to driving continuous improvement.
What is a digital twin?
A digital twin is a dynamic, virtual representation or model of a part, object, machine, or even an entire production process. It consists of the physical item itself, the virtual representation of the item, and the connective functionality that allows data to pass back and forth between the physical and virtual object.
Given its interactive, dynamic functionality, a digital twin goes beyond a simple digital blueprint and includes all of the elements and dynamics of how an object exists and functions in the real world.
This gives humans the ability to see how a physical object would respond under varying conditions and circumstances while leveraging real-time data from the physical world, but in a virtual environment.
In order to collect data, digital twins use a combination of virtual and augmented reality to visualize objects, as well as IoT devices and sensors to collect data from the environment that the physical environment exists in. This ensures that the digital twin accurately depicts an object or process at any given time on the factory floor.
Because of these features, a digital twin used within a manufacturing setting can be applied to a single critical component or an entire machine. In more expanded use, the technology can also be used to map out and keep tabs on an entire production line and even the production operations as a whole.
A brief history of the digital twin
The concept of digital twins was first introduced in the 1991 book Mirror Worlds, written by David Gelernter. In it, he describes “Mirror Worlds” as high-tech voodoo dolls, digital representations of large-scale physical environments (schools, hospitals, cities) that can be manipulated and interacted with using a desktop computer.
While Gelernter’s writings were largely speculative, forecasting what could come in the distant future, it didn’t take long for technologists to begin applying the concept in a very real way. For example, in 2002, Dr. Michael Grieves began finding ways to practically apply the technology within the manufacturing industry, developing a model that applied all of the elements of a digital twin to a product lifecycle management use case.
The name “digital twin” was coined in 2010 by John Vickers in a NASA report titled Technology Area 12: Materials, Structures, Mechanical Systems, and Manufacturing Road Map. Since then, it’s been applied across countless different industries, enabling humans to interact with physical products and environments using a seamless, digital medium.
Digital twin examples
One of the most common examples to explain the concept of the digital twin is a wind turbine. Wind turbines can be used in a number of different environments, both onshore and offshore, in a variety of different climates, temperatures, weather conditions, etc.
This presents a significant challenge for energy companies trying to project how much energy a given wind turbine is able to produce given the significant variability in the physical environment that the turbine is placed in.
In order for the energy company to more accurately project how a turbine will function given the environment it is placed in, they may consider building a digital twin, allowing them to collect real-time data from sensors placed on the physical turbine, feed the data into a digital replica of the turbine, and run various different artificial intelligence models to test varying conditions that the turbine may be exposed to.
This creates a much more accurate forecast of the turbine’s functionality and output over a given period of time.
This same concept can be applied to any number of physical products, giving manufacturers a more accurate way to predict how their products will function given the different environments and conditions they may be exposed to.
How digital twins can be used in manufacturing settings
Different manufacturing operations have diverse requirements and systems specific to their production processes. For this reason, manufacturers can apply digital twin technology in any number of distinct ways, resulting in different results unique to that production entity.
Here are some ways manufacturers leverage digital twin technology to improve thi operations.
Prototype testing and evaluation: Companies using innovative manufacturing techniques choose digital twins over physical prototypes. The former can be manipulated more easily to include changes and rectifications, making product design and enhancement a more efficient process. Integration with other systems also ensures that the testing and evaluation processes are rooted in correct and accurate data.
Improving production system designs: Digital twin applications span further than singular products. Instead, they also include entire production systems.
Manufacturers use digital twins to analyze proposed production line systems and layouts, affording key personnel a more insightful picture before installation begins. For example, manufacturers can use data extracted from digital twins to run production line simulations to identify and rectify any potential kinks in the system.
In addition, manufacturers use digital systems to have an optimized overview of processes outside the factory, including supply and delivery chains.
Equipment monitoring and preventive maintenance: This technology is widely applied to equipment on the factory floor. Digital twins of machines offer an accurate real-time virtual equivalent, providing maintenance personnel with parameters detailing equipment health and performance.
With the AI and machine learning capability of Industry 4.0, digital twins can point out impending failures, allowing manufacturers to get out ahead of them.
Maintenance personnel can then use augmented reality goggles to visualize the accurate models laid over the real machine on the floor. This provides precise form and specifications on which the technician can base his work.
Benefits of digital twins in manufacturing
As the fourth industrial revolution presses on, smart factories are adopting even more technology to make their production operations proceed more profitably. Digital twin technology is one approach that provides numerous benefits to such manufacturing companies.
These benefits include:
Better production efficiency: Production line simulations run through digital twins identify possible bottlenecks in the operation, allowing manufacturers to optimize their system for better productivity and production efficiency.
Improved overall equipment effectiveness: Manufacturers use digital twins for their asset lifecycle management. The constant real-time monitoring approach lets them stay ahead of equipment breakdowns by facilitating predictive maintenance.
Improved product quality: Digital twins play a significant role in product design and prototyping. The technology provides detailed insights that enable manufacturers to craft ideal products for their customers and improve quality management practices.
More efficient logistic management: Using digital twins to plan and monitor the entire operation provides informative insights into the company’s overarching production process. This gives manufacturers better and more efficient control over the supply and delivery chains.
Overall business profitability improvement: These benefits boil down to improved manufacturing business profitability. The company spends less on repair and refitting operations and also satisfies its customers with better products and order fulfillment.
How to build a digital twin
While the concept of digital twins can seem daunting and complicated, the technology can be applied to a number of different use cases with truly significant benefits to manufacturers. And while this technology is just recently becoming more widely adopted across the industry, it is projected that digital twin functionality will become a standard feature in approximately 88% of IoT platforms by 2025.
As the technology becomes more widely available, it makes sense for businesses to understand how to go about building digital twins to support their operations. Here are some recommendations from experts in the industry:
Clearly define the purpose and scope of the project - What are you planning to mirror with your digital twin? How will the technology impact the way you’re currently doing business? Understanding the purpose of implementing any technology, and clearly communicating expectations with key stakeholders is an important practice in any large-scale project.
Understand the tools needed for the project - Building a digital twin requires a wide array of different software and hardware to create an accurate, real-time model that can be interacted with. Equipment can include any number of IoT devices, sensors, and monitors, and software can include 3D CAD solutions to design the model as well as IoT platforms to display the actual twin itself. Understanding the actual application of your digital twin will help inform the different equipment and solutions you’ll need to complete your project.
Here’s how to go about it:
Use computer-aided design (CAD) digital solutions to create virtual 3D twin replicas that can be connected to other manufacturing operation digital tools.
The digital manufacturing tools leverage the Internet of Things to draw data from the equipment on the floor and feed it into the digital twin software in real-time.
This software, known as a game engine, renders the data and applies advanced physics to provide a comprehensible digital twin that reflects the IoT inputs in real-time.
The digital twin software of choice should have version control features. This syncs all digital twin modifications, providing cleared personnel access to the same up-to-date file.
In addition to digital twins, manufacturers are constantly looking for new opportunities to drive continuous improvement within their operations.
Tulip’s intuitive, composable platform enables businesses to track every action as it happens across production lines, sites, and facilities around the world.
If you’re interested in learning how Tulip can help you improve the productivity of your operations, reach out to a member of our team today!
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