SIM Card Connector Inspection
Detect SIM card connector defects against challenging surfaces
Graphical programming environment for deep learning-based industrial image analysis
A SIM card holder is a shallow tray with six or eight contacts that allows a subscriber identity module (SIM) card to be easily inserted or removed while securely holding the card during use. They are used in a range of mobile applications.
The surface of a SIM card holder can have a wide range of defects including scratches, dents, and deformations. These can be difficult to find and identify on the surface, which is often black or another dark color.
Finding these defects is difficult for traditional machine vision, and often requires more than one automated optical inspection (AOI) machine in sequence to achieve even a limited detection capability. Even with multiple AOI machines, only a limited number of defects can be programmed.
Cognex Deep Learning’s defect detection is ideal for detecting anomalies on SIM card connections. The defect detection tool trains on a set of images of defect-free SIM cards as well as a set of images of defective SIM cards. Once trained, it accurately detects a wide range of defects in the connector while passing purely cosmetic marks that do not affect function.
Traditional machine vision can only detect a limited number of defect types that occur in a fixed position, while the Deep Learning defect detection tool detects a broad range of different defects regardless of where they are on the item being inspected. Because of the defect detection tool’s capabilities, it is possible to reduce the number of vision inspection stations required which lowers costs while achieving high levels of accurate defect detection.