Overview
Medical device manufacturing demands extreme precision, where even microscopic imperfections can impact patient safety. A leading medical device producer sought to modernize its automated inspection workflows and strengthen consistency across multiple production sites. By integrating Premio’s AI edge inference industrial computer into its machine vision stations, the company improved performance, scalability, and long-term support compatibility.
Challenges
- CPU performance needed to keep pace with increasing machine vision inference workloads
- Flexibility to support different GPU performance tiers for multiple inspection lines
- Long lifecycle hardware for validated manufacturing environments
- Compliance with TAA along with CE, FCC and UL certifications
- Expansion capability for future storage growth and I/O add-ons
Solution
- Premio’s AI edge inference industrial computer (RCO-6000-RPL-2)
- Equipped with Intel® 13th Gen “TE” processors for real-time machine vision analysis
- RTX2000ADA GPU support for precise defect detection
- Modular EDGEBoost expansion for added connectivity and future vision hardware
- Industrial-grade reliability tailored for continuous inspection workloads
Benefits
- Higher throughput in machine vision inspection
- Long-term reliability with lifecycle stability
- Scalable performance with modular GPU and I/O expansion
Company Overview
A prominent medical device manufacturer specializing in advanced cardiovascular technologies drives innovation in patient-critical equipment. Its operations excel through strict quality control standards and precision-based production processes. The organization continues advancing manufacturing automation to support growth and improve product consistency worldwide.
The Challenges
Rising CPU Demand for Machine Vision
Modern medical device inspection relies on processing high-resolution images at rapid speeds, placing significant demand on edge computing performance. As production volume increased, existing hardware struggled to maintain real-time inference throughput. The company required a stronger CPU platform capable of consistent, deterministic performance across multiple lines.
Supporting Various GPU Requirements
Different inspection stations required different GPU accelerators, ranging from mid-range inference workloads to higher-end configurations. Their previous hardware limited GPU flexibility and made standardization difficult across diverse inspection tools. The company needed a platform capable of accommodating a wide range of NVIDIA GPUs without redesigning the system footprint.
Ensuring Long Lifecycle Stability
Manufacturing environments relying on validated hardware require components that remain available for many years. Frequent changes in system architecture or discontinued parts can disrupt qualification cycles. Long-term supply stability became essential for sustaining uniformity across global production sites.
Meeting Industry Certification Requirements
Regulatory adherence is critical in medical device manufacturing, and all computing equipment must align with strict certification requirements. The company required hardware compliant with TAA guidelines while also carrying CE, FCC, and UL certifications. These certifications ensured safe integration into inspection cabinets and automated work cells.
Scalable Expansion for Future Growth
As machine vision processing evolves, storage capacity and additional I/O requirements can expand rapidly. Static hardware configurations would have limited the company’s ability to integrate future algorithms or sensor hardware. A modular design with room for PCIe additions and storage upgrades became a key operational requirement.
The Solution
Premio RCO-6000-RPL-2 Integration
Premio’s AI edge inference industrial computer delivered the performance foundation for consistent defect detection. Its fanless industrial design allowed seamless installation into inspection enclosures, enabling real-time inference with minimal maintenance. The system became the standardized platform across multiple inspection lines.
High-Performance CPU for Inference Processing
The Intel® 13th Gen “TE” processors provid e the multithreaded performance needed for rapid image analysis. Deployed directly on inspection benches, the processor handled continuous workloads without throttling during peak output. This ensured stable performance even as camera resolutions and inspection complexity grew.
GPU Acceleration with RTX2000ADA
The RTX2000ADA GPU enabled accurate detection of micro-defects in intricate medical components. Its CUDA performance helped accelerate neural network inference within each machine vision station. This hardware consistency improved detection repeatability across different manufacturing lines.
Modular I/O and Storage Expansion
EDGEBoost add-on modules ensured the system could adapt to future inspection requirements. Additional PCIe hardware allowed integration of more sensors, frame grabbers, or expanded SSD storage without redesigning the workstation. This modularity supported long-term operational flexibility.
Industrial-Grade Rugged Reliability
The rugged fanless architecture ensured dependable performance in factory-floor conditions where dust, vibration, and round-the-clock runtime are common. These systems operated continuously with minimal maintenance interventions. Local support from Premio’s Los Angeles location ensured quick response for lifecycle and configuration needs.
The Benefits
Greater Inspection Efficiency
AI-accelerated inference improved line throughput, allowing more products to be inspected with higher accuracy.
Lifecycle and Compliance Confidence
Long-term availability and required certifications aligned seamlessly with the company’s regulated manufacturing environment.
Scalable Modular Platform
Expansion capability supported new inspection techniques and higher data volumes without requiring complete system replacements.
Conclusion
By integrating Premio’s RCO-6000-RPL-2 AI edge inference industrial computer, the medical device manufacturer modernized its automated inspection processes while maintaining compliance and lifecycle stability. The upgrade strengthened defect detection consistency and created a scalable foundation for future machine vision advancements. The result is a more reliable, future-proof inspection infrastructure that aligns with the high standards of medical device production.