Currently, surface defect detection of stamping grinding flat parts is mainly undertaken through observation by the naked eye. In order to
improve the automatic degree of surface defects detection in stamping
grinding flat parts, a real-time detection system based on machine
vision is designed. Under plane illumination mode, the whole region of
the parts is clear and the outline is obvious, but the tiny defects are
difficult to find; Under multi-angle illumination mode, the tiny defects
of the parts can be highlighted. In view of the above situation, a
lighting method combining plane illumination mode with multi-angle
illumination mode is designed, and five kinds of defects are
automatically detected by different detection methods. Firstly, the
parts are located and segmented according to the plane light source
image, and the defects are detected according to the gray anomaly.
Secondly, according to the surface of the parts reflective
characteristics, the influence of the reflection on the image is
minimized by adjusting the exposure time of the camera, and the position
and direction of the edge line of the gray anomaly region of the
multi-angle light source image are used to determine whether the anomaly
region is a defect. The experimental results demonstrate that the
system has a high detection success rate, which can meet the real-time
detection rEquation uirements of a factory.To get more news about
lighting hardware, you can visit tenral.com official website.
With the mass production of parts, the inspection of product quality is
very important during the process of parts production. The traditional
detection methods of surface defects rely on manual detection, and they
suffer from an inherently low degree of automation and low detection
efficiency, and the entire inspection process is subjective. With the
development of automation technology, the detection of surface defects
of parts has gradually changed from manual detection to machine
detection, in which machine vision is a very popular detection method
[1,2,3].
