Industrial Automation

Digital Twins and Edge Computing Unite to Drive Manufacturing Insights

Digital twins and edge computing are powerful on their own. But when you put them together, you unlock even more possibilities.


Manufacturers are beginning to grasp the benefits of digital twins, simulating outcomes based on real conditions without having to carry those simulations out in real life. (You can get the full details in our recent blog about the application of digital twins in Industry 4.0.)


But challenges stand in the way of manufacturers being able to use digital twins to gain a big-picture view of what’s happening on the factory floor. These includes concerns about:

  • Data privacy and security
  • Costs
  • Accuracy

One way to overcome these challenges is to leverage the capabilities of digital twins and edge computing.

Digital Twins Defined

As a refresher, a digital twin is a virtual replica of a physical object, system or industrial asset that provides a real-time representation of status, behavior and performance. With a digital twin, manufacturers leverage data from sensors, IoT devices and other sources to monitor, analyze and optimize processes.


Integrating digital twins and edge computing can help address the challenges that many manufacturers face regarding data privacy, security, costs and accuracy, providing a clear path forward.


4 Benefits of Integrating Digital Twins and Edge Computing

Digital twins and edge computing are powerful technologies on their own. But when you put them together, you can unlock even more possibilities. Here are four examples.


1. Reduce Response Time

A digital twin represents a physical system. Integrating edge computing enables real-time data processing and minimizes latency since data is processed close to the source. As a result, manufacturers can make decisions quickly. For example, they can quickly respond to what they see from sensors and IoT devices.


2. Optimize Data

IoT devices and sensors capture massive volumes of data that can bog down processes. Edge processing gives manufacturers the ability to process only the relevant information that needs to go to the cloud or to servers.


3. Enhance Security

Because edge computing processes sensitive data locally, the risk of unauthorized access is reduced. This helps manufacturers keep cybersecurity at the forefront of their Industry 4.0 transformation projects.


4. Lower Risk

Because edge computing handles complex computations locally, more intricate modeling and simulation capabilities are possible. With a narrow gap between digital twin simulations and real-world intricacies, decision-making risk is reduced.

4 Ways Digital Twins Are Used at the Edge

The use cases for digital twins are nearly limitless, and they can make use of edge computing in several ways. These use cases represent what manufacturers are doing today to capitalize on the benefits of integrating these technologies, using them to enhance production and maintenance.


1. Deploy Predictive Maintenance

When an automotive manufacturer experienced downtime due to unexpected machine failure, digital twins fueled by edge computing were used to predict equipment failures before they happened. This was possible through continuous monitoring and analysis of simulated real-time data from sensors. When the digital twin detects the potential for abnormal vibrations in a robotic arm, it triggers a maintenance alert so that someone from the team can take a closer look.


2. Optimize the Supply Chain

By using digital twins to create a virtual representation of the entire automotive supply chain, automotive manufacturers can monitor real-time simulations on inventory, production status, logistics and more. Because the data is processed locally, manufacturers can make decisions faster to improve their supply chain.


3. Boost Quality Control

Quality and safety are critical for food and beverage manufacturing, which makes it difficult to implement changes without creating risk. Employing digital twins to create virtual representations of production processes, standards and variables, like temperature, humidity, and ingredient proportions, gives food and beverage manufacturers a picture of production without compromising product development in real life.


4. Enable Energy Management

Managing energy consumption in manufacturing is critical to maintain operational efficiency. Digital twins that are powered by edge computing provide simulations and patterns in real-time so manufacturers can identify energy-intensive processes and implement energy-saving processes to meet sustainability and cost reduction goals.


Data Modeling Is the Foundation of Digital Twins

Data modeling is essential for developing the digital twins that serve as digital blueprints that mirror the physical world.


Belden can help you address these needs with our user-friendly, efficient approach to data modeling for digital twins. We simplify the process, making digital twins accessible even to users who don’t have extensive experience with programming or data science, by enabling:

  • Immediate data usage. Users can start modeling their data as soon as it’s collected, laying the groundwork to create effective digital twins.
  • Versatile data utilization. Our platform enables the same physical asset to be modeled differently. One team may want to use the data model for predictive maintenance, while another wants to use it for production optimization or other data science activities. The versatility we provide allows different departments to utilize data tailored to their requirements, allowing them to create and view different data models of the same asset for their specific purposes.

  • Data model creation. Once data is collected from a source like a PLC, machine or other industrial asset, you can create a data model that correlates with the actual equipment or asset. This step is essential in transforming raw data into a structured format that’s useful to you for analysis and decision-making.

  • Integration and deployment. We make it easy to integrate and deploy your data models. With just a few clicks, you can transmit data to platforms like Azure IoT Hub or store it in a database.

  • Reduced data processing requirements. Once a specific data model is defined at the edge, it reduces the need for additional data processing. The system enables the creation of tailored datasets that are ready to be used by various applications without further manipulation.


Integrating edge computing with digital twins is a huge step forward for process improvement. Industries like automotive, food and beverage, healthcare and others stand to benefit immensely by realizing efficiency, reliability and sustainability improvements.


The continuous advancement of edge computing technologies and the refinement of digital twin models will present new opportunities for innovation and optimization. Embracing the innovation that digital twins and edge computing offer will help you stay competitive in an increasingly competitive global market.


Learn more about Belden's industrial automation solutions.


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