Edge AI with NVIDIA Jetson Orin NX delivers 157 TOPS edge AI visual inspection for automotive PCBA lines, achieving 100% inspection and 99.7% detection.
Case Details
EA Series Brings AI Visual Inspection to the Production Line Edge
A leading automotive electronics parts manufacturer produced PCBA and precision connectors on high-speed lines. Its inspection process relied on manual visual checks and simple optical equipment. As line speed increased, the approach could not keep up: sampling inspection left missed-detection rates above 1.2%, consistency stayed below 92%, and labor costs remained high.
Previous attempts also fell short. Industrial PC-based vision systems recognized only a dozen preset defects and produced an 18% false alarm rate. A cloud AI pilot added 1.5–2 seconds of latency and stopped inspection during network fluctuations—one carrier failure halted a line for 47 minutes, causing over RMB 120,000 in losses.
EA Series On-Device AI Visual Inspection Deployment
Beilai Technology deployed its EA250Pro industrial edge AI computer as a fully local inspection platform.
- Compute: NVIDIA Jetson Orin NX 16GB, up to 157 TOPS INT8, with an 8-core Cortex-A78AE CPU, 1024 CUDA cores, and 32 Tensor Cores.
- Vision: 4 GigE Vision industrial cameras via a VD01 expansion board, with hardware trigger and strobe synchronization; 5MP global shutter at up to 84 fps.
- Line integration: Dual Gigabit Ethernet, isolated RS485/RS232, CAN FD, and digital IO connect directly to the PLC and MES. Results trigger rejection mechanisms in real time.
- Industrial design: Fanless aluminum chassis, -20°C to +75°C operation, 12–24V input, vibration and EMI resistance, mounted directly on the line rack.
- Software: JetPack 6.2.2 and TensorRT-optimized YOLOv8. Single-frame inference is under 10 ms; the full acquisition-to-IO process stays within 50 ms. Models are trained in the cloud and delivered via OTA; raw images stay on site.
One EA250Pro runs four inspection tasks simultaneously—solder joints, component missing/misplacement/reversal, surface scratches, and dimensions—replacing four separate devices. Deployment used an “in-situ replacement” model: one workstation went live in 6 hours, and 12 stations across 3 lines were operational within a week.
Results After Six Months
- Coverage: 100% full inspection, up from 25% sampling; 27 defect types; minimum detectable defect 0.03 mm.
- Accuracy: Detection rate above 99.7%; false alarms down from 18% to 2.8%; consistency up from 92% to 99.5%.
- Speed: About 80 ms per piece; 45,000 inspections per hour per station, covering the line’s 3,000 pieces/hour takt with headroom.
- Labor: Inspection staff reduced from 4 to 1 per line—9 fewer inspectors across 3 lines, saving about RMB 720,000 annually.
- Quality: Customer returns from missed defects down 62%, saving about RMB 580,000 per year.
- O&M: Annual failure rate 0.3 times per unit; downtime maintenance cut from 32 hours to under 3 hours.
- Payback: About 8.5 months; three-year ROI above 340%.
- Management: Full quality traceability through MES; model iteration shortened from 2 weeks to 3 days; all inference stays local for data compliance.
Conclusion
EA series is not a generic compute box. It combines industrial-grade Jetson compute, native camera and industrial IO, and production-line integration to close the loop from perception to decision to execution. The result: higher inspection coverage and accuracy, lower labor and quality costs, and a deployable path to intelligent manufacturing.
