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A visual online detection method and system for powder coating quality of laser selective melting forming

A technology of laser selective melting and detection method, which is applied in the direction of optical testing for flaws/defects, etc., can solve the problems of difficult promotion and high cost of infrared thermal imaging cameras, and achieve significant economic benefits, reduce production costs, and improve quality.

Active Publication Date: 2019-07-19
HUAZHONG UNIV OF SCI & TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the cost of infrared thermal imaging cameras is relatively high, and the wavelength sensitivity of the camera needs to be selected according to different powder materials during use. Therefore, it is necessary to know the reflected light wavelength of the powder materials used before forming, which is not easy to popularize.

Method used

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  • A visual online detection method and system for powder coating quality of laser selective melting forming
  • A visual online detection method and system for powder coating quality of laser selective melting forming
  • A visual online detection method and system for powder coating quality of laser selective melting forming

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Experimental program
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Effect test

Embodiment 1

[0066] Referring to Figure 5(a), it can be seen that there are high cladding layer defects (block defects) in this figure, which are processed by using the above-mentioned adaptive double threshold segmentation method. The steps of segmenting block defects are as follows:

[0067] (1) Select 200×200 sub-blocks to traverse each pixel of the image, and perform the following processing on each pixel in the image:

[0068] G out =G in -μ l +μ src

[0069] Among them, G out is the processed pixel gray value, G in is the pixel gray value before processing, μ l is the mean value of the gray value of all pixels in the current sub-block, μ src is the mean value of the gray values ​​of all pixels in the original image;

[0070] (2) Obtain the mean value μ and standard deviation σ of the image pixel gray level after processing;

[0071] (3) Traverse each pixel of the image, when the gray value of the pixel is greater than μ+2σ or less than μ-2σ, set the gray value of the pixel t...

Embodiment 2

[0074] Referring to Figure 5(a), it can be seen that there are stripe-shaped defects (horizontal line defects) in this figure, which are processed by the above-mentioned adaptive double threshold segmentation method. The steps of segmenting horizontal line defects are as follows:

[0075] (1) Select 5×30 sub-blocks to traverse each pixel of the image, and process each pixel in the image as follows: G out =G in -μ l +μ src

[0076] Among them, G out is the processed pixel gray value, G in is the pixel gray value before processing, μ l is the mean value of the gray value of all pixels in the current sub-block, μ src is the mean value of the gray value of all pixels in the original image;

[0077] (2) Obtain the mean value μ and standard deviation σ of the image pixel gray level after processing;

[0078] (3) Traversing each pixel of the image, when the pixel gray value is greater than μ+1.5σ or less than μ-1.5σ, set the pixel gray value to 255, otherwise set it to 0.

...

Embodiment 3

[0081] Referring to Figure 5(a), it can be seen that there are long strips of powder pile defects (vertical line defects) in this figure, which are processed by the above-mentioned adaptive double threshold segmentation method. The steps of segmenting vertical line defects are as follows:

[0082] (1) Select 30×5 sub-blocks to traverse each pixel of the image, and perform the following processing on each pixel in the image:

[0083] G out =G in -μl +μ src

[0084] Among them, G out is the processed pixel gray value, G in is the pixel gray value before processing, μ l is the mean value of the gray value of all pixels in the current sub-block, μ src is the mean value of the gray values ​​of all pixels in the original image;

[0085] (2) Obtain the mean value μ and standard deviation σ of the image pixel gray level after processing;

[0086] (3) Traversing each pixel of the image, when the pixel gray value is greater than μ+1.5σ or less than μ-1.5σ, set the pixel gray val...

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Abstract

The invention discloses a visual online detection method and system of SLM (selective laser melting) formed spreading powder quality to automatically detect and recognize defects of spreading powder. The detection system comprises an industrial camera, a camera lens, a lighting source and a data processor. After SLM forming of spreading powder is completed, a surface image of the spreading powder is acquired through the industrial camera and transmitted to the data processor. The data processor performs extraction and recognition on defects sequentially through image processing, mode recognition and other methods. Automatic detection of the SLM formed spreading powder quality is realized with a machine vision method, the quality of a finally formed part is improved by ensuring the quality of each layer of spreading powder, the rejection rate of formed parts is effectively reduced, the production cost is reduced, and the economic benefits are remarkable.

Description

technical field [0001] The invention belongs to the field of machine vision and image processing, and in particular relates to a visual online detection method and system for powder coating quality of laser selective melting forming. Background technique [0002] Powder spreading is an important link in the forming process of Selective Laser Melting (SLM), and the quality of powder spreading directly affects the quality of formed parts. Different defects often appear in the powder spreading process, such as stripe-shaped defects, long-strip-shaped powder pile defects, massive powder pile defects, insufficient powder supply defects, and high cladding layer defects, etc., which may cause the final formed parts to be waste products. Due to the high cost of forming a single part by laser selective melting, in order to ensure the quality of the part, it is necessary to detect the powder coating quality of each layer during the processing. At present, the quality of powder coatin...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01N21/95
CPCG01N21/95
Inventor 王泽敏张鹏杨晶晶杨慧慧李祥友曾晓雁
Owner HUAZHONG UNIV OF SCI & TECH
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