In majority of traditional AOI technology, are using camera to record the light of line laser and structured light. However the 3D imaging principle and scheme of the light field camera is different from the line laser and structured light. It can obtain multi-view, multi-focal plane, and multi-dimensional information, and realize functions such as single camera, one shot, multiple focus, multiple perspectives, three-dimensional measurement/modeling, etc. ..
4D light field camera Inspection
The light field camera does not need to actively project laser or structured light, and is less interfered by ambient light and occlusion. It has more advantages in the three-dimensional detection of photosensitive materials, transparent/translucent materials, and surface reflective materials, and solves glass, thin films, tiny metals, etc. The problem of fast and high-precision detection of 3D defects in complex and high-precision scenes.
Sealing nail of square aluminum shell power battery-defect Inspection
Defect detection requirements
Small pinholes (requirements ≥0.1mm must be detected, the smallest size in history can be detected at 0.06mm
..
Defect detection requirements 2
Crack (crack along the weld)
..
Defect detection requirements 3
Spatter/melt beads (may be splashed out of the welding zone)
..
Precision measurement: coin surface
The minimum height of pattern protrusions is 4μm
..
..
..
..
..
..
Accuracy of scratch recognition >97%
Improve recognition accuracy
Full field data
Every detail is clear
Iterative Autonomous Iterative Evolutionary Algorithm
Scratch recognition results match the original data
High-contrast magnified display of all scratches
According to scratch depth Accurately identify printing, texture, scratches, and stains Accuracy is dynamically adjusted according to needs
On a fixed production line, every time an algorithm is iterated independently, the surface detection and recognition rate will increase, and the machine learning speed is thousands of times that of humans. Based on the continuous accumulation of production data, the accuracy and accuracy of automated recognition will be rapidly improved. rate Traditional machine vision differential comparison only has a fixed recognition algorithm. To improve the recognition rate requires heavy computing power development, which is time-consuming and labor-intensive, and it is difficult to optimize the production line in real time.
Precision measurement: flexible surface
Non-contact measurement of flexible surfaces
Widely compatible with materials and environments
High-speed dynamic measurement of 3D data
The copper contact is reflective, the structure is fine, and the output is huge
Need to detect the depth of 16 points in one second
Unable to measure with traditional contact
The laser structured light has huge distortion on the reflective surface
Using 4D light field to achieve 1 micron depth precision measurement
More than 50 times per second