Eureka AIR delivers breakthrough ideas for toughest innovation challenges, trusted by R&D personnel around the world.

Aramid paper honeycomb gluing defect detection method

A technology of defect detection and aramid paper, applied in the field of visual inspection, can solve problems such as no uniform pattern, poor imaging, waste of raw materials, etc., and achieve the effect of increasing the number of samples, reducing pressure, and strong generalization ability

Pending Publication Date: 2020-06-05
CHINA PRECISION ENG INST FOR AIRCRAFT IND AVIC
View PDF3 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] 1. There are many types of defects, and the defect forms are complex and diverse, and there is no unified model
Therefore, the pattern recognition method based on traditional artificial feature extraction cannot effectively adapt to a variety of defect types.
[0004] 2. The defect scale changes greatly and the aspect ratio changes drastically
Therefore, the image analysis and processing methods in general inspection methods cannot adapt to the size of the analysis area, so that they cannot effectively detect defects of different scales and aspect ratios.
[0005] 3. In the paper itself and the actual working environment, there is a lot of noise interference, and the detection based on the visual method is easily affected by problems such as dust / light / imaging
Therefore, the existing defect detection methods based on features or templates cannot effectively avoid the influence of noise, which can easily lead to misidentification and waste of raw materials
[0006] 4. In the actual industrial working environment, it is not easy to obtain samples. It is difficult to obtain a large number of samples of defects of different types and shapes, and sample marking will waste a lot of time
Therefore, detection methods based on sample libraries and big data cannot play a good role in factories

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
  • Aramid paper honeycomb gluing defect detection method
  • Aramid paper honeycomb gluing defect detection method
  • Aramid paper honeycomb gluing defect detection method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0020] Specific embodiments of the present invention will be discussed in detail below in conjunction with the accompanying drawings.

[0021] combine figure 2 , image 3 As shown in the decoder and encoder, the present invention provides a method for detecting aramid paper honeycomb gluing defects, including aramid paper, on which there are defects generated in the gluing process; industrial cameras, used for The defect image information of aramid paper is obtained; the depth feature encoder is used to fully encode the defect image information of different types and shapes; the feature decoder is used to accurately locate the encoded defect image information; the detection steps are as follows: figure 1 detection protocol workflow,

[0022] The first step is to perform image enhancement processing in different ways on the defect image information obtained by the industrial camera to obtain different defect image samples;

[0023] In the second step, the deep feature encod...

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

According to the aramid paper honeycomb gluing defect detection method provided by the invention, aiming at various defect problems generated in the aramid paper honeycomb gluing production process, adeep network is designed, defect characteristics are automatically extracted, and pixel-level positioning is realized; for the problem that samples are difficult to obtain, a large number of samplesare automatically generated through an image enhancement algorithm, and therefore dependence of machine learning on the number of samples is reduced. The detection scheme provided by the invention solves the problem of simultaneous detection of defects of different sizes and different length-width ratios, is accurate in positioning and high in detection efficiency, and can meet the requirement ofautomatic detection of aramid paper honeycomb gluing defects.

Description

technical field [0001] The invention belongs to the technical field of visual detection, and in particular relates to a method for detecting aramid paper honeycomb glue coating defects. Background technique [0002] During the honeycomb gluing process of aramid paper, there will be six kinds of defects, including black spots, lack of glue on the glue strip, holes, incomplete glue strips, impurities and wire drawing. If these defects cannot be processed in time, it will affect the subsequent honeycomb manufacturing process. At present, the detection of this defect mainly relies on human eye observation. This method is inefficient, labor-intensive, poor in reliability and prone to errors. A vision-based automatic detection method for honeycomb gluing defects of aramid paper. The detection of images by traditional machine learning methods mainly relies on manual feature extraction. However, the defect detection of aramid paper honeycomb has the following characteristics, so th...

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
IPC IPC(8): G06T7/00G06T5/00G01N21/88
CPCG06T7/0004G01N21/8851G06T2207/10004G06T2207/20081G06T2207/30108G01N2021/8887G06T5/00
Inventor 王琳琳李树刘京亮罗松保陈钱李喆
Owner CHINA PRECISION ENG INST FOR AIRCRAFT IND AVIC
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Eureka Blog
Learn More
PatSnap group products