Deep learning-based molten pool image geometric feature extraction method and system

A technology of geometric features and extraction methods, applied in the field of laser welding technology and deep learning, can solve problems such as low accuracy of molten pool extraction and inconsistent image brightness, and achieve strong anti-interference ability, accurate edge information, and good anti-interference ability.

A technology of geometric features and extraction methods, applied in the field of laser welding technology and deep learning, can solve problems such as low accuracy of molten pool extraction and inconsistent image brightness, and achieve strong anti-interference ability, accurate edge information, and good anti-interference ability.

CN113554587APending Publication Date: 2021-10-26JIANGSU UNIV

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  • Deep learning-based molten pool image geometric feature extraction method and system
  • Deep learning-based molten pool image geometric feature extraction method and system
  • Deep learning-based molten pool image geometric feature extraction method and system

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[0080] The overall flow chart of the method for extracting geometric features of molten pool images based on deep learning in the present invention is as follows figure 1 As shown, it includes the following steps:

[0081] Step S101: Reference figure 2 The schematic diagram of on-line monitoring of laser welding is shown, using the Qianyanlang 5KF20 high-speed camera and its equipped infrared lighting system and filter system to collect images of the molten pool during the laser welding process, and from the dynamic video of the molten pool every 5 frames Extract the molten pool image to obtain a clear original image set of the molten pool;

[0082] Step S102: Using image processing algorithms such as image grayscale and Gaussian filtering algorithm to image 3 The original image of the molten pool shown in (a) is filtered and denoised to reduce the interference of salt and pepper noise in the image. The image processing results are as follows image 3 as shown in (b);

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Abstract

The invention provides a deep learning-based molten pool image geometric feature extraction method and system. The method comprises the steps of image acquisition, image preprocessing, image data set making, image segmentation network model construction and training, molten pool image online monitoring and molten pool geometric feature extraction and calculation. According to the invention, the morphology and evolution behavior of the molten pool in the laser welding process can be clearly observed, and interference information is less; according to the invention, a U-Net image segmentation network is used for extracting a molten pool area for laser welding for the first time, an improved eight-direction Sobel edge detection algorithm is adopted for extracting edge information, and the extracted edge information is more accurate; the molten pool area image extraction algorithm based on deep learning has strong anti-interference capability; compared with a traditional image processing algorithm, interference of splashing and plume can be better eliminated from the image, the anti-interference capability is good, and robustness is strong.

Description

technical field [0001] The invention relates to the field of laser welding technology and deep learning, in particular to a method and system for extracting geometric features of molten pool images based on deep learning. Through the image segmentation algorithm in the field of deep learning, the information of the laser welding molten pool area is accurately extracted, and the geometric features of the molten pool are extracted based on this. Background technique [0002] As one of the important application fields of laser processing technology, laser welding is a method of welding with the heat generated by bombarding the welding area with high-energy laser beams as energy. It has the advantages of less heat input, high welding precision, good quality, thermal deformation of joints and Due to the advantages of small heat-affected zone, it is widely used in aerospace, metallurgical machinery and other fields. In laser welding, the keyhole and its surrounding molten metal p...

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Application Information

Patent Timeline
26 Oct 2021
Publication
CN113554587A
IPC
G06T7/00; G06T7/11; G06T7/13; G06T7/194; G06T7/62; G06N3/04; G06N3/08; G06T5/00
CPC
G06T7/0004; G06T7/11; G06T7/13; G06T7/194; G06T7/62; G06N3/08; G06T2207/20081; G06T2207/20084
Inventors
许桢英; 李奇灵