Smart manufacturing is the integration of advanced digital technology and automation system, thus realizing the intelligent and efficient management of production process. However, traditional industrial controllers have been difficult to meet these demands, so there is an urgent need for a new type of edge computing controller to fulfill the requirements of smart manufacturing. As a key interface between information technology (IT) and operation technology (OT), edge computing controller plays an important role in realizing edge computing, data processing and device control.
Edge Computing Controller Architecture
A typical edge computing controller usually consists of three main components: device layer, edge layer, and cloud layer.
1. Device Layer
The Device Layer consists primarily of sensors and controllers that are responsible for directly collecting and controlling information in the physical world. Typical devices include smartphones, self-driving cars, robots, and factory equipment. They capture data through sensors and perform specific actions through controllers. These devices generate a large amount of data which is first sent to the edge layer for initial processing. Function:
Data capture: continuous monitoring of device status or environmental variables (e.g., temperature, speed, light, etc.) via sensors.
Execution control: Trigger relevant operations, such as device start/stop or function adjustment, through the controller.
2. Edge Layer
The Edge Layer is the first line of defense for data processing. Through edge nodes/servers, this layer is responsible for real-time processing, filtering, streamlining, and caching of data from the device layer to reduce latency and improve response time. In some cases, edge devices can make decisions directly based on algorithms without having to transmit all data to the cloud. Function:
Data Processing and Simplification: compresses, filters, and preprocesses raw data generated at the device layer to preserve core data.
Data Buffering and Caching: Responds to sudden data traffic and prevents bottlenecks in the cloud due to data overload.
Control Response: Make instant decisions locally, especially for application scenarios with high requirements for low latency, such as autonomous driving and industrial control.
Virtualization: Virtual machines or containers can be run in the edge layer for unified management and control of multiple devices.
3. Cloud Layer
The cloud layer is the top layer of the whole architecture and is mainly used for big data processing and data warehousing. Cloud servers receive simplified and processed data from the edge layer and then utilize more powerful computing resources for deeper data analysis, model training and storage management. The cloud is also responsible for long-term storage of global data and computing for complex tasks, such as training and optimization of AI models. Function:
Big data processing: cloud servers are capable of processing large amounts of data from multiple edge nodes and performing complex operations and analysis.
Data Warehouse: stores massive amounts of data and provides query and analysis functions for historical data.
Global interconnection: connects edge servers around the world via the Internet to realize collaboration and data sharing on a global scale.
Key technologies for edge computing controllers
Industrial Internet of Things (IIoT)
Industrial Internet of Things (IIoT) is an application of the Internet of Things (IoT) in the industrial field and is the core of IoRT. IIoT enables seamless communication and collaboration between robots, sensors, and other devices, laying the foundation for automation and intelligence. Sensors such as temperature, pressure, proximity, motion and force/torque play a key role in monitoring manufacturing equipment and processes, providing valuable insights into operational efficiency, predictive maintenance and product quality.
1. Internet of Things at the Edge
IoT at the edge supports real-time decision-making, reduces latency, increases bandwidth efficiency and improves overall reliability. Edge computing ensures uninterrupted production and minimizes disruption, even when network connectivity is intermittent or limited. It also improves data privacy and security because sensitive data can be processed locally.
2. Programmable Logic Controller (PLC)
PLCs act as automation coordinators in the IoRT ecosystem, managing and coordinating interconnected devices, machines, and systems.PLCs provide real-time data that provides decision makers with valuable information to perform proactive maintenance and optimize resource allocation. They play a key role in seamless data exchange and communication between devices, streamlining and synchronizing production workflows.
3. Sensors
Sensors are the “eyes” and“ears” of the IoT , enabling robots to interact with and understand their surroundings. Temperature, pressure, proximity, motion and force/torque sensors play a key role in monitoring manufacturing equipment and processes, providing valuable information about operational efficiency, predictive maintenance and product quality.
4. Generative Artificial Intelligence
Generative Artificial Intelligence is a technology that can leverage existing data and patterns to create novel designs, solutions, and optimization strategies that can revolutionize robot design and automate robot movements and manufacturing processes.
5. Integration with ERP systems
Integrating IoRT with Enterprise Resource Planning (ERP) systems provides a holistic approach to manufacturing management, with the ERP system acting as the central hub for resource planning, inventory management and production scheduling. By connecting IoRT data to ERP systems, manufacturers can gain real-time production insights, optimized inventory management, enhanced predictive maintenance, seamless production planning, and quality control and compliance benefits.
6. Human Machine Interaction (HMI) and User Experience (UX)
HMI and UX play a key role in maximizing the potential of IoT. Intuitive and user-friendly interfaces enable seamless interaction between operators and robotic systems, increasing productivity and shortening the learning curve, enhancing the overall user experience.
Conclusion
As a key interface between IT and OT, the edge computing controller plays an important role in realizing edge computing, data processing and device control. Its layered architecture makes it scalable and reusable, and can be applied to many types of industrial equipment control applications. The convergence of key technologies such as collaborative robotics, computer vision and artificial intelligence, industrial IoT, edge IoT, PLC, sensors, data analytics, and generative AI gives the edge computing controller powerful functionality. At the same time, the integration with ERP system and the optimization of HMI and UX further enhance the application value of edge computing controller in intelligent manufacturing. In the future, with the continuous development of technology, edge computing controller will play a more important role in the field of industrial automation and intelligent manufacturing.