What is the core technology of AOI automatic optical detector?

AOI is an emerging new testing technology, but it is developing rapidly, and many manufacturers have launched AOI testing equipment. During automatic inspection, the machine automatically scans the PCB through the camera, collects images, compares the tested solder joints with the qualified parameters in the database, after image processing, checks out the defects on the PCB, and displays/marks the defects through the display or automatic signs Come out for repair by maintenance personnel. During automatic inspection, the machine automatically scans the PCB through the camera, collects images, compares the tested solder joints with the qualified parameters in the database, after image processing, checks out the defects on the PCB, and displays/marks the defects through the display or automatic signs Come out for repair by maintenance personnel. AOI (area of interest), that is, the information surface, also called the interest surface, is an area-like geographic entity in map data.

AOI core technology

The basic AOI technology includes the following subsystems: ①High-speed and high-precision XY motion control system; ②Mechanical optical system; ③High-precision and high-reliability image acquisition system; ④Intelligent image recognition and error detection system. These subsystems constitute a device closely related to multi-dimensional measurement and error detection.

Note that AOI recognition is a specific application of machine vision in the field of printed circuit boards. In other words, the defect detection of printed circuit boards is essentially a category of pattern recognition. It treats different defects on the PCB as different pattern types, extracts and selects features from the collected image signals, constructs a discriminant function according to the feature vector, and performs defect classification, that is, pattern recognition.

The quality of the recognition algorithm directly affects the performance of the intelligent image recognition system, which in turn affects the performance of the entire AOI system. From the perspective of the development of machine vision, at least the following visual recognition algorithms can be fully applied to AOI.