What is a Digital Twin and its Role in Industry 4.0?

In simple terms, a digital twin is a copy of a real-world object in digital space, however, it's not just a CAD model or simulation. A digital twin models the entire lifecycle of a product or process to include design, manufacturing, and operational characteristics. It is essentially a highly detailed representation of every aspect of a product or process. There are four levels of digital twins as described below.

1. Component Level - The least complex digital twin focuses on a single component (usually a critical component) within a product or system.

2. Asset Level - This level focuses on an entire product that may be built from multiple components. Interactions between the components are monitored.

3. System-Level - This level focuses on a collection of assets and can include the analysis of a production line with complex interactions. For example, a system-level digital twin must monitor all the machinery on a production line to analyze performance.

4. Process Level - This focuses on a manufacturing process from start to finish. Essentially the entire product lifecycle is included in the digital twin so design, manufacturing, sales, and distribution logistics all have digital parts to play.

One of the features that can make a digital twin so effective is its integration with real-world sensor data. This data can be extracted from an IoT sensor in a piece of equipment or macro sources like production-volume data, sales data, etc. A digital twin is fed real-time data from a real-world product or system which will then be used to replicate the system’s performance in the virtual world. The integration with sensors and other sources of data allows for digital twin models based on past, present, and future data.

• Past - Historical data generated from systems or products can be used to optimize existing products or inform the design of future products.
• Present - Real-time data collected during the operation of the component or system is employed to monitor performance and ensure that it remains within acceptable limits.

Future - New insights and predictions can be generated off existing datasets by making use of machine learning algorithms.

Digital twin modeling can be broken down into two main categories.

1. Physics-based modeling can simulate future what-if scenarios and is developed from first principles. These models can also use historical data to refine and validate the model.

2. Data-driven modeling can estimate system performance by using real-time sensor data to update the prediction models.

Modern oil platforms are excellent examples of real-world implementation of the digital twin concept. Once an oil platform has been installed it needs to be continuously monitored to ensure it is still structurally sound and functioning as designed. Normally, this monitoring would be done in the form of regular physical inspections. That process is, however, time-consuming, expensive, and only ever captures snapshots of the system characteristics. This is where digital twins excel. 

If the same oil rig has IoT sensors in all critical locations, the real-time data extracted can instead provide an up-to-date picture of all the structure’s vitals. If it is approaching its design limits, the data gained can also be used to predict potential future problems and allow preventative action to be taken.

As IoT sensors become cheaper, general data-gathering processes become more efficient, and the adoption of Industry 4.0 marches ever on, digital twins are becoming similarly popular. There are a number of advantages to using digital twins, some of which are described below.

• A digital twin can improve users’ understanding of assets in real-world applications. The data gathered can then be used to optimize future designs or processes. The amount of data gathered by IoT sensors in real-time creates rich datasets that can be further used to generate new insights.
• Operation and maintenance costs can, in some cases, exceed the cost of the original asset. As such, it is worthwhile to optimize these systems. One method of doing so involves embedding IoT sensors in manufacturing equipment to pick up excessive vibration or heat buildup which can indicate imminent failure. This can help improve the efficiency of preventative maintenance programs.
• Systems or products can be simulated in unique environments before deploying any real-world changes. This can be done with an existing product that may need to be deployed in a different untested environment or be used as part of the design process for new products. The digital twin can be exposed to new environments to detect any potential performance issues. The information gathered from this process can then be used to update the real-world product or system with significantly less risk.
• Digital twins allow for more efficient conceptual development of new products. Unlike traditional simulation that just tests a small section of a design in isolation, the twin allows one to test the entire product as a system in a chosen environment with realistic parameters.

Implementing a digital twin is not a simple task and requires a thorough understanding of a product's intended use and the underlying physics of its operation. High-quality IoT sensors are required to relay real-time data to the digital twin and advanced software systems are needed to make sense of this data. Once properly implemented, a digital twin can offer unparalleled insight into how a system interacts with its surroundings and opens the doors for improved performance, reduction of waste, and greater profitability. Digital twin modeling is one of the key technologies of Industry 4.0 and is definitely the future of smart manufacturing. To learn more about digital twins and how they might be applied to your own processes and products, contact a Xometry expert today.