Digital Twins How Real-Time Electronics Optimize the Creation of Digital Twins

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Thanks to data-driven real-time replicas of physical systems, manufacturers can optimize processes and predict machine failures using digital twins. However, the effectiveness of digital twins requires precise real-time data.

Mark Patrick is Director of Technical Content, EMEA at Mouser Electronics.

From embedded systems and gateways for the Internet of Things (IoT) to precision sensors: electronics form the foundation of the digital twin ecosystem. These electronic components collect, process, and transmit critical information that powers the twin, enabling the feedback loop necessary for predictive maintenance and adaptive manufacturing. In this blog post, we explain how electronics make digital twins possible, the role of key components in the process, and why data security is the cornerstone of smarter and more resilient manufacturing processes.

What Are Digital Twins And Why they Are Important

A digital twin is a virtual representation of a physical object or system. In manufacturing, this can range from a single machine to an entire production line. Unlike static models or simulations, digital twins are dynamic. They continuously receive data from the real world, reflecting the current state, performance, and environment.

This real-time mirroring enables several advanced functions. Predictive maintenance becomes possible by identifying equipment issues before they lead to failures. Manufacturers can optimize processes by identifying inefficiencies or bottlenecks. Using test scenarios with digital twins, developers can model changes or virtually stress-test systems before implementation. Additionally, remote diagnostics facilitate monitoring equipment at multiple or hard-to-reach locations.

Digital twins also support a more agile and responsive manufacturing environment. Instead of relying on outdated plans or estimates, manufacturers can make decisions based on current data. This reduces downtime, improves product quality, and helps lower operating costs.

By creating virtual real-time replicas of physical assets, digital twins are changing the way manufacturers monitor, maintain, and optimize their operations.

Real-Time Data: The Foundation of Digital Twins

Digital twins are only as reliable as the data they are provided with. To accurately reflect the physical world, they require a constant flow of information that mirrors the current conditions surrounding the object or system. In manufacturing, key conditions include temperature, humidity, pressure, flow rates, vibration, motion, and machine status.

Electronics provide the tools to capture and transmit this information. Sensors measure these variables directly at the source. Embedded systems process the data locally, and IoT gateways handle the communication between components and digital platforms. Together, these technologies ensure that the digital twin remains synchronized with its physical counterpart.

What makes this flow of information so effective is the continuous feedback loop it creates. When conditions in manufacturing change, the digital twin reflects these changes in real time (Figure 1). Conversely, the insights generated by the digital twin, such as warnings about wear patterns or performance deviations, can be used to adjust the operation of the physical system before problems occur.

The Importance of Electronics for Digital Twins

At the core of every application of digital twins is a tightly integrated network of electronics that captures, processes, and transmits operational data. These electronic components must not only function reliably in demanding environments but also meet specific technical performance requirements.

Sensors are the first interface in data collection. For this reason, sensors must be precise, sensitive, and durable to provide meaningful real-time measurements. In manufacturing, this can include detecting slight temperature changes, pressure fluctuations, or early signs of mechanical vibrations, all of which can indicate the need for intervention.

Embedded systems are responsible for analyzing this raw data. These systems often employ edge computing techniques to filter, process, and compress information before it is transmitted. This distributed processing helps to reduce latency and enables faster, localized decision-making, particularly in time-critical applications such as fault detection or safety warnings.

IoT gateways and communication modules play a crucial role in ensuring that data reaches its destination. Whether the digital twin platform is on-site or in the cloud, the network infrastructure must support real-time updates with minimal interruptions. Connectivity challenges, such as temporary signal loss, bandwidth limitations, or cybersecurity risks, must be addressed with robust hardware and secure protocols.

Finally, the systems must be designed to ensure data security at every point in the signal chain. Even minor distortions, delays, or losses can affect the accuracy of a digital twin and diminish its value. Therefore, developers must rely on highly efficient components from the outset and design for reliability (Figure 2).

Building A Reliable Infrastructure for Digital Twins

The effectiveness of a digital twin ultimately depends on the components that connect it to the physical world. There is a wide range of hardware solutions that enable digital twins to be implemented robustly and in real time. These include the following examples from Mouser Electronics' product portfolio.

The X300 from Lantronix is a compact cellular IoT gateway designed for use in demanding industrial environments. It provides secure connections over LTE Cat 1, NB-IoT, and Cat M1 networks. This gateway plays a critical role as a bridge for communication between distributed sensor networks and cloud-based platforms. With its integrated edge processing capabilities, the X300 can preprocess data before transmission, optimizing network bandwidth and reducing latency. It is particularly suitable for applications where wired connections are not practical or where mobile assets or devices need to be continuously monitored.

The SSD 250A from Bourns is a high-precision current sensor for monitoring DC and pulse current levels. Its compact design and robust construction make it particularly suited for industrial applications, especially where monitoring energy consumption is crucial for maintenance and efficient operation of equipment. In a digital twin, it provides reliable data to detect current deviations and analyze energy consumption.

The SN-GCQB1 from Panasonic is a compact and responsive sensor specifically designed for detecting gas concentrations and particles. It offers fast response times and high sensitivity, which is essential in manufacturing environments where even small changes in air quality can affect machine reliability or product consistency. By feeding environmental data into a digital twin, this sensor helps identify potentially disruptive conditions before they impact operations.

The combination of these components demonstrates how important the selection of the right hardware is for capturing high-precision data and maintaining real-time responsiveness, which makes digital twins so effective in modern manufacturing (Image 3).

The Success of Digital Twins Depends on Suitable Data And Electronics

Digital twins are changing the way manufacturers manage their operations, perform maintenance, and make decisions. However, their success depends on accurate and up-to-date data, with electronics playing a crucial role.

By integrating precise sensors, robust embedded systems, and secure IoT gateways into the infrastructure of digital twins, manufacturers can create responsive, data-driven systems that provide comprehensive insights and measurable improvements. Components such as the X300 from Lantronix, the SSD 250A from Bourns, and the SN-GCQB1 from Panasonic, available from Mouser's portfolio, meet the reliability and performance requirements essential for these next-generation manufacturing solutions.

With the right foundation, digital twins not only reflect reality but also actively contribute to shaping it, enabling manufacturers to achieve greater efficiency, resilience, and adaptability for the future. (mk)

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