Cybord has published a white paper detailing how its QCI (Quality Component Inspection) solution leverages bottom-side image capture during pick-and-place operations to inspect electronic components in real time. The system applies machine learning and computer vision algorithms to detect damage, corrosion, and lead irregularities before components are mounted onto PCBs, aiming for 100% inline inspection coverage with full traceability data.
The development of the technology was driven by a documented production crisis in which a faulty 3-cent capacitor caused failures in a large-scale defense project. Cybord founder and CTO Dr. Eyal Weiss has stated that identifying the root cause of such component-level failures without inline inspection data proved extremely difficult, underpinning the case for integrating AI-based quality control directly into the SMT assembly process rather than relying on post-placement AOI alone.
By positioning inspection at the pick-and-place stage, Cybord's approach targets counterfeit and substandard components earlier in the assembly flow than conventional AOI or AXI systems, which typically operate post-reflow. The solution is software-based and designed to integrate with existing SMT equipment, making it relevant for contract manufacturers and OEMs seeking enhanced component-level traceability.
Quelle: Cybord – https://cybord.ai/news_events/news/cybord-inline-visual-ai-analytics-identifies-defective-components-on-the-fly/
