Industry 4.0 introduces the use of virtual models to forecast the behavior of processes under specific conditions. Traditionally, these simulations relied on preset conditions. However, with the integration of real-time data and wireless connectivity, the "digital twin", a virtual model of a system, accurately reflects a process by adapting to actual conditions and evolving scenarios.
How can digital twins enhance factory automation? Let’s look at an example from Boeing, one of the largest aerospace manufacturers, using digital twin technology to simulate their engine components’ performance.
- Design and Planning: The aerospace industry is a highly regulated industry where precision of every component is critical. Sensors, cameras, and IoT devices are strategically placed throughout the plant with the goal of predicting product components’ performance in varying conditions. AI is used to analyze the collected data to create a virtual replica, or “twin”, of the plant and go through a simulated lifecycle of the environments and conditions.
- Real-Time Decisions and Data Integration: incorporating wireless sensors such as temperature, vibration, and shock onto existing equipment gathers accurate, real-time data that the digital twin can incorporate into its models. This is essential as aerospace engine components, such as compressors and turbine blades, are monitored within tightly specified behavioral tolerances.
- Quality Control and Risk Mitigation: creating a digital twin helps predict how a new aircraft will perform. Collecting data from sensors and using machine learning to identify patterns for potential quality issues enables predictive maintenance. For example, identifying potential issues in casting machines can help save the facility from disastrous scenarios if parts fail due to equipment failure.
- Security and Compliance: The aerospace industry has stringent regulatory requirements, so specific machine parameters, testing results, and material specifications is proprietary. With numerous hardware components such as sensors, actuators, and other equipment sending wireless communications to the digital twin, data security is vital. Data encryption and authentication are needed so having built-in encryption like WPA3 and secure boot mechanisms help meet these security standards.
Let’s dive in deeper how a factory uses digital twin technology:
The data collected from sensors is integrated into the digital twin to create a real-time virtual model of the manufacturing plant. IoT sensors are equipped with Bluetooth and/or LoRaWAN for local and wide-area network connectivity to capture environmental and equipment data. For example, the Sentrius BT510 sensor, integrating Laird Connectivity’s BL654 Bluetooth module, allows the sensor to wirelessly send data via Bluetooth LE to a gateway (such as our MG100 series) or edge computing device for analysis. At times, sensors transmit data directly to the cloud for processing, however, using a gateway or edge device can reduce latency and save bandwidth as not all nodes will be connected directly to the cloud.
The Laird Connectivity RM126x module, based on Silicon Labs EFR32 series SoC, provides a low-power, long-range LoRaWAN communication. It includes LoRa Point to Point capability, enabling private ultra-long range radio networks between modules for continual real-time information. Similarly, the BL654 module, leveraging the LE Coded PHY feature of Bluetooth 5.1, enables manufacturers to establish longer range Bluetooth Low Energy (BLE) communication for data transmission in environments of any size. Utilizing BLE helps to preserve the sensor’s battery life and maintenance costs for accurate monitoring and predictive maintenance.
Since an edge device gathers vast amounts of sensor data, it needs to process data on-site instead of sending it unfiltered to the digital twin. It utilizes a powerful SOM, such as Laird Connectivity’s Summit SOM 8M Plus, to help interpret the data. Using a NXP i.MX 8M Plus processor and Cortex-M7 microprocessor, which has a real-time operating system (RTOS), the edge device can process data at the edge before sending it to the digital twin via Wi-Fi. The neural processing unit used in the Summit SOM 8M Plus delivers up to 2.3 TOPS for high-performance AI and machine learning. For instance, if a misalignment in an electronic component on a production line is detected, the edge device can immediately send a command to flag the defective component. With multiple high performance memory options up to 4GB LPDDR4, there is enough memory to handle data from multiple sensors.
As data is transferred from numerous sensors to edge devices and the digital twin, it needs to be secure. The Summit SOM 8M Plus incorporates Summit Suite which is ideal for connected solutions that need strong security architectures. Summit Suite has a range of software services like secure connectivity with WPA3-Enterprise and chain of trust device to ensure that the information is safe. Incorporating data encryption, accessing vulnerabilities, and verifying device authenticity can be managed through Laird Connectivity’s Summit Suite to protect devices with multiple layers of security software.
As factories transition into smarter, more efficient facilities, digital twin technology will play a pivotal role in shaping the future of factory automation.