Overview
Across modern automotive manufacturing, production facilities are rapidly evolving toward Industry 4.0, where real-time machine data and predictive analytics are essential for maintaining productivity and minimizing downtime. A European automotive parts manufacturer operating dozens of CNC machines and robotic systems required a scalable way to monitor equipment health and production performance directly on the factory floor. By deploying Premio’s compact industrial edge computing platform, the manufacturer enabled real-time machine monitoring, improved predictive maintenance capabilities, and integrated legacy factory equipment with modern manufacturing systems.
Challenges
• Limited processing capability for real-time machine data collection and analytics at the edge
• Insufficient connectivity for integrating CNC machines, robotic systems, PLCs, and industrial sensors
• Lack of high-speed networking to transmit machine data reliably to MES and SCADA platforms
• Industrial PCs were too large and costly for deployment inside individual machine control cabinets
• Difficulty integrating legacy factory equipment into modern Industry 4.0 monitoring infrastructure
Solution
• Premio’s compact industrial 3.5-inch single board computer (CT-DAS01)
• Intel Atom x7835RE and x7433RE processors for efficient edge data processing
• Industrial communication interfaces including RS232 RS422 RS485 and CAN FD for machine connectivity
• Dual 2.5GbE LAN ports for factory network and MES integration
• M.2 NVMe storage support for local machine data logging and analytics
Benefits
• Real-time machine monitoring across the factory floor
• Reduced downtime through predictive maintenance insights
• Scalable deployment across multiple machine cells
Company Overview
Operating within the European automotive manufacturing supply chain, the organization specializes in producing high-precision mechanical components used in vehicle assemblies. Advanced CNC machining and robotic assembly technologies form the backbone of its high-volume production environment. As the company continues expanding its smart manufacturing initiatives, it focuses on integrating digital monitoring and predictive analytics into every stage of production.
The Challenges
Limited Edge Processing for Machine Data
Modern manufacturing equipment generates large volumes of operational data, including spindle speeds, vibration signals, and temperature readings. However, traditional monitoring approaches relied on centralized systems that introduced delays when processing machine data. Without sufficient local processing at the machine level, detecting anomalies in real time became difficult.
Complex Connectivity Across Industrial Equipment
The production floor included a diverse mix of CNC machines, robotic arms, PLC-controlled stations, and industrial sensors. Many systems relied on different communication protocols, including serial interfaces and industrial fieldbus technologies. Integrating these devices into a unified monitoring platform required a controller capable of supporting multiple industrial communication interfaces.
Data Transmission Bottlenecks to MES Platforms
Machine data needed to be transmitted to the Manufacturing Execution System and SCADA platforms for monitoring and analysis. Existing systems lacked sufficient network bandwidth to reliably handle the growing volume of operational data. This limitation slowed down data visibility and reduced the effectiveness of factory-wide monitoring systems.
Deployment Constraints Inside Machine Cabinets
Installing monitoring systems directly inside machine control cabinets required hardware with a compact footprint. Traditional industrial PCs were too large and expensive to deploy at every machine cell. As a result, many machines operated without dedicated monitoring devices.
Integrating Legacy Equipment into Industry 4.0 Infrastructure
Many CNC machines and PLC systems were designed long before Industry 4.0 initiatives became standard in manufacturing. These legacy machines lacked native support for modern data platforms and cloud-based analytics. Bridging the gap between legacy equipment and digital factory systems required a flexible edge computing platform capable of supporting older industrial communication protocols.
The Solution
Premio’s Industrial Edge Computing Platform
Premio’s CT-DAS01 3.5-inch SBC industrial motherboard
To address the factory’s monitoring challenges, the manufacturer deployed Premio’s CT-DAS01 3.5-inch SBC industrial motherboard as a smart edge controller across multiple machine cells.
The board’s compact 146 × 102 mm footprint allows it to be installed directly inside machine control cabinets, enabling scalable deployment without requiring major modifications to existing machine enclosures.
Installed at the machine level, the system collects operational data from CNC machines, PLCs, robotic systems, and industrial sensors while serving as an edge gateway connecting legacy equipment to modern manufacturing systems.
Efficient Edge Data Processing
Intel Atom x7835RE and x7433RE processors
The CT-DAS01 provides efficient edge processing using Intel Atom x7835RE and x7433RE processors, enabling real-time machine data collection and localized analytics.
The board supports DDR5 SO-DIMM memory up to 32GB, allowing industrial applications to run data analysis workloads directly at the machine level.
By processing machine telemetry locally, manufacturers can perform functions such as anomaly detection, cycle-time monitoring, and equipment health tracking before transmitting data to centralized MES or SCADA platforms.
Industrial Communication Integration
2x RS-232/422/485 and 2x CAN FD interfaces for machine connectivity
The CT-DAS01 provides extensive industrial connectivity through two RS-232/422/485 serial interfaces and two CAN FD interfaces, enabling integration with a wide range of factory equipment.
These interfaces allow the edge controller to connect to legacy PLC systems, industrial sensors, and CNC machine controllers while also supporting communication with modern robotic systems and motion control devices.
This flexible I/O capability allows manufacturers to integrate older production equipment into Industry 4.0 monitoring systems without replacing existing machines.
High-Speed Factory Network Connectivity
Dual 2.5GbE LAN ports
The system features dual 2.5GbE Ethernet ports, enabling high-bandwidth connectivity for transmitting machine data to factory automation networks and enterprise systems.
One network interface can connect to the machine-level control network, while the second interface connects to Manufacturing Execution Systems (MES) or enterprise IT infrastructure. This configuration ensures reliable data flow while maintaining segmentation between operational technology (OT) and enterprise IT networks.
Local Data Logging and Storage
M.2 NVMe storage support
The CT-DAS01 supports high-speed storage through an M.2 M-key slot supporting NVMe SSDs, enabling local data logging and edge analytics.
The board also includes an M.2 B-key slot supporting NVMe storage or 4G/5G cellular modules, with SATA shared on the M.2 B-key interface.
This flexible storage and connectivity architecture enables detailed machine data histories to be stored locally while supporting optional wireless connectivity for remote factory deployments.
Industrial-Grade Design for Factory Environments
The CT-DAS01 is designed for demanding industrial environments with features including:
• Wide 9–36V DC power input compatible with factory power systems
• Operating temperature range from –40°C to 85°C
• TPM 2.0 hardware security support
• Dual Nano SIM sockets for cellular connectivity
• Triple independent display support for industrial visualization systems
These capabilities allow the board to operate reliably in industrial automation environments while supporting modern security, networking, and visualization requirements.
The Benefits
Real Time Factory Visibility
Machine data from CNC systems, robotic arms, and sensors is collected and processed in real time. Operators gain immediate visibility into machine performance, enabling faster decision-making on the factory floor.
Reduced Downtime Through Predictive Maintenance
Edge analytics detect abnormal machine behavior before it leads to equipment failure. Maintenance teams receive early alerts that allow them to address issues before they disrupt production schedules.
Scalable Smart Factory Deployment
The compact single-board computer design enables installation inside individual machine cabinets across the production line. As a result, the manufacturer can scale monitoring coverage across dozens of machines without major infrastructure changes.
Conclusion
As manufacturers transition toward Industry 4.0 environments, real-time machine monitoring and predictive analytics are becoming essential for maintaining efficient operations. By deploying compact edge controllers throughout the factory floor, the manufacturer successfully integrated legacy equipment with modern digital infrastructure. Premio’s CT-DAS01 provided the industrial connectivity, processing capability, and scalability required to support the next generation of smart factory operations.