X-ray welding seam image defect grading method and system

Abstract

The invention discloses an X-ray welding seam image defect grading method and system. The grading method comprises: acquiring an X-ray welding seam image; determining the type, position and size of the defect of the X-ray weld joint image according to the X-ray weld joint image; and determining the defect grade of the X-ray welding seam image according to the type, position and size of the defect.According to the grading method, full automation of X-ray welding seam image defect detection and grading is achieved, missed judgment and misjudgment caused by manual judgment are avoided, and the grading precision is improved.

Description

technical field [0001] The invention relates to the field of defect grading, in particular to a method and system for grading defects in X-ray weld images. Background technique [0002] In the field of welding defect detection, X-ray inspection is one of the important methods commonly used in conventional nondestructive testing. At present, in actual production, the main method is to manually analyze the weld image, and determine whether there are defects and the type, location, size, etc. of defects based on experience, so as to evaluate the welding quality and give corresponding ratings. The manual evaluation method is affected by human factors such as personal technical level, experience, fatigue, emotion and external conditions, which is inefficient, unreliable, and poor in consistency, and the strong light of the film reading is easy to burn people's eyes. Modern machinery manufacturing has higher and higher requirements for precision, and manual operation is increasin...

AI Technical Summary

Problems solved by technology

These two methods only use deep learning methods to classify and identify defect candidate areas. The generation of defect candidate areas still uses traditional image processing

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