Detection device and method for surface defects

Abstract

The invention provides a detection method for surface defects. The detection method for surface defects comprises the following steps: the surface frequency spectrum of a metal machining part to be detected and the surface frequency spectrum of a metal machining part free of surface defects are subtracted, so as to obtain a frequency spectrum of defects; the frequency spectrum of defects is actually the frequency spectrum on which the frequency spectrum of the surface background of the metal machining part to be detected is removed; therefore, when the inverse Fourier transform is conducted on the frequency spectrum of the defects, the surface image on which the surface background of the metal machining part to be detected is removed is obtained; the surface image is displayed, the situation that whether the surface of the metal machining part to be detected has defects or not and the distribution of the defects can be clearly distinguished. Therefore, the detection device and the detection method for the surface defects can weaken the interferences of the surface background of the metal machining part to the detection process; the effect of the defect detection is improved and the speed of defect detection is increased. Moreover, the invention further provides a detection device for the surface defects.

Description

technical field [0001] The invention relates to defect detection technology, in particular to a surface defect detection device and method. Background technique [0002] Anodizing is a common processing technology for metal machined parts. The air bubbles generated during the anodizing process may solidify on the metal surface, thereby affecting the quality of metal machined parts. The traditional detection method is to detect defects manually. This method is slow, the detection efficiency is low, and subtle defects cannot be detected visually. Moreover, during the processing of metal machined parts, regular twill lines or patterns are generally formed on the surface, resulting in complex surface backgrounds of metal machined parts. However, when manually detecting defects, the complex background on the surface of the metal machined parts will affect the identification of defects, which makes the effect of defect detection poor and the detection efficiency is not high. Co...

AI Technical Summary

Problems solved by technology

The traditional detection method is to detect defects manually. This method is slow, the detection efficiency is low, and subtle defects cannot be detected visually.

Moreover, during the processing of metal machined parts, regular twill lines or patterns are generally formed on the surface, resulting in complex surface backgrounds of metal machined parts.

However, when manually detecting defects, the complex background on the surface of metal machined parts will affect the identification of defects, which makes the effect of defect detection poor and the detection efficiency is not high

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