Reduce recalls by using visual data and AI to detect defective parts

Live webinar: The power of AI and big data—how visual data enables surgical traceability

Defective and counterfeit parts, and other component faults such as parts that are recycled, tampered with, damaged, or expired, are known to cause 8 in 10 product failures and significantly raise the risk of recalls.

This is a huge problem, particularly in today’s market, since major shortages in the component supply chain are forcing manufacturers to work outside of their trusted supplier network and are exposing them to significant risk of component faults. Existing traceability solutions don’t provide sufficient component-level protection since they only test small samples and can’t identify problems in individual components in a mixed batch. That’s why now, more than ever, electronics manufacturers need a component inspection solution that can provide visual verification of every component before it is placed on a PCB.

Siemens partner Cybord—a startup that helps electronics manufacturers improve product reliability and material sourcing—has tackled the problem by developing a first-of-its kind solution that enables visual inspection of 100% of the components on a PCB. The SaaS solution utilizes existing images taken by SMT assembly machines and pairs them with proprietary AI to determine whether each and every component is authentic and in good condition in real time. Since it is a SaaS solution and utilizes existing images, no additional hardware is required and there is no impact on speed. It enables surgical traceability of defective parts and counterfeit components, allowing manufacturers to minimize costly recalls.

Join the upcoming Siemens webinar with Cybord on July 20th, 2022 to learn more about this new method to secure the electronic component supply, as well as mitigate risk and secure traceability using the latest technology.

Register for the live webinar now>>

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This article first appeared on the Siemens Digital Industries Software blog at