In manufacturing, innovative concepts and pilots often run ahead of the technology available to actually support them. The use of a digital twin in manufacturing is one such concept. A digital twin is an idea that has been growing for years and is just now realizing its full potential—thanks to the availability of faster communication, reliable infrastructure and more easily accessible data storage.
What is a digital twin?
Digital twin technology empowers manufacturers to test, monitor, engineer, and customize a one-to-one virtual replica of a component or product, whether in development or in service. Digital twin applications in manufacturing have expanded vastly in recent years, with uses at every stage of the industrial cycle, from initial conception to production and on to the actual usage of the product. Through the rest of this piece, we will explore in greater depth the uses of a digital twin in the manufacturing industry, explaining the functions and benefits.
Digital twins in manufacturing are among the most innovative implementations of Industry 4.0 technology, creating true-to-life, real-time access and collaboration from anywhere through IoT sensors and high-speed communications technology. As machine learning, AI and the Industrial Internet of Things become more prominent, digital twins are evolving to integrate these technologies. Combined, these become even more accurate and true-to-life, making digital twins as natural as any other element of the manufacturing space. Building a smart factory entails incorporating all this technology—and more—to drive digital transformation and the benefits that it creates. The smart factory and digital twins go hand-in-hand in bringing an advanced manufacturing facility up to date with the latest and most powerful technology in use today.
How does a digital twin work?
A digital twin works by replicating, in a virtual environment, the form and function of a physical part, product or component. As supporting technology for digital twins has grown, the use of this concept has expanded to include in-service parts and products, as well as production concepts for engineering or customization. Digital twin solutions are becoming more widely used thanks to greater availability of high-speed communication networks, inexpensive data storage and increased adoption of technology such as monitoring sensors.
In the product development phase, digital twins can be virtually “handled” and manipulated much like a real-world part would be, but without the need to actually produce or even prototype the product. This represents a vast step forward from technology such as 3D modeling, allowing engineers and development personnel to get the most tactile, accurate representation of a product without the need to produce anything in the physical realm.
Here is a brief rundown of the process steps involved in creating and utilizing digital twins in a manufacturing environment:
1. Defining the physical asset or process: The machine, product, line, or system to be twinned is selected.
2. Connecting data sources: Engineering data, sensor data and operating data for the asset in question are gathered.
3. Creating the virtual model: The digital version is developed to replicate the asset’s structure, function or behavior.
4. Updating the model: Data collected from IIoT sensors and operational systems feed the twin to keep it true-to-life.
5. Simulating and monitoring outcomes: Users test changes, evaluate performance or identify risk through simulations with the virtual twin.
6. Making decisions: Findings from the digital twin simulations are used to support maintenance, engineering or process decisions.
Digital twins for in-service parts and products fulfill a different function. These digital twins are powered by advanced sensor technology, which provide immense amounts of data in near-real time. This ability allows maintenance personnel, engineers and others to test tasks such as calibration and maintenance, and even measure the impact of adjusting machine speeds or shutting down equipment.
What data powers a digital twin?
As effective as they can be, digital twins ultimately are only as useful as the data that powers them. Some of the most critical datasets that go into building a digital twin are:
- Engineering or CAD data
- IIoT sensor data
- Machine health data
- CMMS and work order data
- Production data
- Quality data
- Environmental data
Additionally, CMMS and machine health data are critical for maintenance operations because they help connect equipment behavior to actual failures, repairs and asset history. Digital twins should not exist in a vacuum—they need to be connected to operational systems and maintained over time. This is because asset records, sensor data and work order history must be kept as accurate as possible to prevent weak insights or misleading information about maintenance and processes.
Benefits of digital twins
Digital twins provide numerous benefits, including:
- Reduced production and prototyping costs: Digital twins in the development and manufacturing processes can help reduce costs and pay back investments by decreasing the need for real-world product creation and handling. Applications here include a digital twin model against which to test quality and performance off the production line; more flexible and inexpensive prototyping without the need to create an actual part; and easier manufacturability and product quality testing prior to production.
- More accurate and effective maintenance: With digital twins, personnel can get the most accurate view of performance and potential maintenance issues without needing to monitor or handle in-service equipment in person. This allows for more efficient use of resources; greater accuracy in maintenance functions; and earlier identification of problems, which can then be addressed proactively.
- Greater innovation in engineering: Digital twins are, in some ways, the next stage in product design and modeling. To date, they provide the most realistic representation of an in-development product. The credibility of a digital twin to the actual product allows designers to develop and test various innovative approaches. Ultimately, this allows engineers to make more informed decisions about whether to scrap or move forward with an idea—unlocking even greater potential for design breakthroughs.
- Easier customization and testing: Digital twins allow for broad and versatile customization options, which can be easily demonstrated to customers and then moved into production. In addition, digital twins enable incredibly accurate product testing, providing critical data about how a given part or component will work in an existing assembly.
- Training and simulation: Another area in which digital twins continue to prove their value to manufacturing operations is in the realm of employee training. The technology makes it possible for new hires to become familiar with equipment without the need to work on a real-world example that would have to be taken offline. It also gives maintenance technicians a safe space to hone their skills without putting real machinery at risk.
How digital twins support predictive maintenance
Digital twins can be an important tool for predictive maintenance efforts. This is because predictive maintenance relies on understanding how equipment condition changes and what those changes could mean. Digital twins model assets under real operating conditions, allowing maintenance teams to evaluate possible failure scenarios. As condition data including vibration, temperature, current draw, pressure, flow, load, and speed change, the digital twin can show whether the asset is drifting out of normal operation. This gives maintenance and reliability teams insights into any possible actions that may be needed to keep the asset in good working order.
Examples of how machine health data enables digital twins to support preventive maintenance include:
Machine health signal | How a digital twin can support maintenance |
Vibration changes | Helps evaluate bearing wear, imbalance or misalignment risk |
Temperature increases | Helps assess overheating, friction or cooling issues |
Current draw changes | Helps identify overload or abnormal mechanical resistance |
Pressure or flow changes | Helps evaluate pumps, valves or process instability |
Speed or cycle time changes | Helps identify performance loss or bottlenecks |
Maintenance history | Helps compare current behavior to past failures |
It’s important to note, however, that digital twins should not take the place of technicians or reliability experts. They can improve decision-making by giving teams a more complete view of equipment behavior. The most value comes from connecting digital twin insights to maintenance workflows, work orders, MRO planning, and corrective action.
Digital twins vs. 3D modeling
Although digital twins may sound similar to 3D models made with CAD and other visualization software, they aren’t the same thing. While a CAD model or static simulation of a machine provides a 3D representation of the equipment, digital twins model much more than its physical dimensions. A digital twin is in every respect a virtual reality recreation of the machine, simulating its functionality and even reacting in a predictable manner to variables such as worn components, extreme temperatures and excessive loading.
Applications of digital twins
Digital twin smart manufacturing technology has a broad range of applications throughout the industry today. These include:
- Operational process optimization: Digital twin industry 4.0 technology makes it possible to carry out testing, design and prototyping processes with different pieces of equipment from different locations. This eliminates the need to transport equipment and parts among various areas of the facility, as well as different sites. A facility can test fits and interactions between an in-house piece and a component from a supplier on the other side of the world, in real-time and with total accuracy. This provides one of the greatest cost savings across all IIoT trends, reducing handling and transportation costs and reducing wear and tear.
- Quality control management: With digital twins, QC managers and personnel can compare parts against an exact representation of the piece as it should be. Additionally, they can use real-time production information via heads-up display or other methods. These applications yield vast benefits in production accuracy, safety and efficiency.
- Predictive maintenance: The use of a digital twin for predictive maintenance can yield immense cost benefits, productivity improvements and longer equipment life. Industrial sensors can detect underlying operating conditions that could lead to potential maintenance issues. These conditions can then be replicated real-time in a digital twin for troubleshooting and diagnostics—all without shutting down the equipment. This enables maintenance personnel to make more informed, effective decisions while vastly reducing downtime.
- Supply chain management: The benefits of the smart factory and digital twins go beyond the production floor. Digital twins can also be used to assess and track equipment and part condition, aiding in forecasting and ordering. Digital twin technology can also make it much easier to source proposals for new suppliers.
- Cross-department collaboration: With digital twins, departments can collaborate more easily and effectively than ever—without the need for costly and time-consuming handling and transport of parts and equipment. Digital twins also help to ensure unparalleled accuracy in design and maintenance.
- Engineering/design: With a digital twin, engineers and designers can create a true-to-life prototype without the cost and turnaround time of producing a physical piece. Because the digital twin can interact with other digital models and data, it is just as effective as if the part were right there.
Embrace smarter maintenance with digital twin simulations
It is clear that digital twins—and advancing technology in general—are driving major developments in the manufacturing industry, including maintenance functions. They can help manufacturers move from static models and reactive decisions to more connected, data-powered operations. When combined with IIoT sensors, machine health monitoring and predictive maintenance workflows, digital twins provide maintenance teams with a clearer view of asset performance and how changes may impact them.
At ATS, we go beyond mere theory. Our predictive maintenance programs are powered by digital twins and real-time data to help manufacturers future-proof their operations. For more information, contact ATS today.
