Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Method for automatically identifying and grading central segregation defect of low-magnification structure of continuous casting billet

A technology of low-magnification tissue and automatic identification, which is applied in neural learning methods, optical testing for flaws/defects, and material analysis. The effect of short time and small amount of computation

Active Publication Date: 2019-01-11
NORTHEASTERN UNIV
View PDF4 Cites 18 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At present, the detection and rating of central segregation of low-magnification defects in continuous casting slabs generally adopts visual inspection, because there are multiple textures on the continuous casting slab, and the area of ​​central segregation itself is not large compared with the continuous casting slab surface At the same time, there are also factors such as cracks, oil stains, shrinkage cavities and other interference

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Method for automatically identifying and grading central segregation defect of low-magnification structure of continuous casting billet
  • Method for automatically identifying and grading central segregation defect of low-magnification structure of continuous casting billet
  • Method for automatically identifying and grading central segregation defect of low-magnification structure of continuous casting billet

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0072] The method for automatic identification and rating of segregation defects of low-magnification structure centers in continuous casting slabs provided by the present invention includes the following steps:

[0073] S1: Preprocessing the grayscale image of the low-magnification structure of the continuous casting slab, including distortion correction, cutting white background and filtering;

[0074] In the present invention, the grayscale histogram corresponding to the grayscale image of the low-magnification structure of the continuous casting slab is selected for defect identification. According to the histogram, the grayscale distribution on the low-magnification structure image of the continuous casting slab can be known, which further clarifies that the present invention needs to identify The central segregation position of the slab, and try to remove the interference in the continuous casting slab image in the preprocessing stage.

[0075] The grayscale image of the...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention provides a method for automatically identifying and grading the central segregation defect of the low-magnification structure of a continuous casting billet. The method comprises the following steps: S1, preprocessing the gray image of a low-magnification structure of a continuous casting billet, including distortion correction, cutting white background and filtering; S2: obtaining acentral area image by segmenting the gray image for the central area where the area of the horizontal direction (2 / 5-3 / 5) and the area of the vertical direction (2 / 5-3 / 5) of the gray image; S3, detecting and labeling the connected region in the binary image corresponding to the central region image to separate the suspected central segregation region from the crack region in the connected region;S4, carrying out feature extraction on that suspected central segregation region, identifying the central segregation region and removing the interference region; S5: training the BP neural network classifier model to identify the center segregation region and grade the same. The technical proposal of the invention solves the problem that the traditional digital image processing method is not suitable for the defect detection of the continuous casting billet whose surface defects are jumbled and liable to cross.

Description

technical field [0001] The invention relates to the technical field of continuous casting slab defect detection, in particular to an automatic identification and rating method for continuous casting slab low-magnification structure center segregation defects. Background technique [0002] In the process of steel production, it is necessary to track and monitor the internal quality information of the produced slab. The detection method of the internal quality of the slab is to observe the low-magnification sample after the acid etching of the continuous casting slab to determine the type and level of surface defects. At present, the method of inspection and rating of low-magnification defects of cast slabs still adopts the manual rating mode, that is, the pictures of low-magnification samples of cast slabs are compared with the national standard YB / T 4002-2013 "Continuous Casting Billet Low-magnification Structural Defects Grading Chart" for visual inspection or manual measur...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T7/11G06T7/13G01N21/88G06N3/08G06T5/00G06T5/30
CPCG06N3/084G06T5/30G06T7/0004G06T7/11G06T7/13G01N21/8851G01N2021/8887G06T2207/20084G06T2207/20032G06T2207/30116G06T5/70
Inventor 孟红记王健宇
Owner NORTHEASTERN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products