Multi-label spot welding electrode cap state automatic identification method based on deep learning
A spot welding electrode, automatic recognition technology, applied in neural learning methods, character and pattern recognition, details involving image stitching, etc., can solve the problems of waste, electrode caps without a set of normative methods, electrode caps not replaced in time, etc. , to achieve the effect of high accuracy, fast recognition speed, and improved manufacturing efficiency and reliability
Pending Publication Date: 2022-05-17
CHONGQING UNIV
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Problems solved by technology
There is no standard method for the state detection of electrode caps, which easily leads to the fact that the electrode caps that should be replaced are not replaced in time, and some of them are in good condition but are replaced due to the number of spot welding, resulting in waste
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[0032] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, so that those skilled in the art can better understand the present invention and implement it, but the examples given are not intended to limit the present invention.
[0033] Such as figure 1 As shown, it is a flow chart of the method for automatically identifying the state of the multi-label spot welding electrode cap based on deep learning in the present invention. In this embodiment, the method for automatically identifying the state of the multi-label spot welding electrode cap based on deep learning includes the following steps:
[0034] 1) Collect the original image of the appearance of the spot welding electrode cap: the original appearance image collected for the same spot welding electrode cap includes a top view of the end face, a side view and oblique views in two directions; the specific image data collection process depends on the o...
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The invention discloses a multi-label spot-welding electrode cap state automatic identification method based on deep learning. The method comprises the following steps: 1) collecting an original appearance image of a spot-welding electrode cap, wherein the original appearance image collected for the same spot-welding electrode cap comprises an end face top view, a side view and oblique views in two directions; 2) obtaining a detection image of the spot-welding electrode cap: for the same spot-welding electrode cap, respectively cutting and zooming an end face top view, a side view and oblique views in two directions which are acquired into set sizes, and splicing into one image to obtain the detection image of the spot-welding electrode cap; 3) setting the number of batch detection images: setting the number of each batch of detection images according to the total number of the obtained detection images; and 4) automatically identifying the appearance multi-label category of the spot-welding electrode cap by using the convolutional neural network model to obtain a detection image and a multi-label category matrix.
Description
technical field [0001] The invention belongs to the technical field of image detection, in particular to an automatic recognition method for multi-label spot welding electrode cap states based on deep learning. Background technique [0002] Resistance spot welding is an important welding method. During welding, columnar electrodes are used to form solder joints between the contact surfaces of two overlapping workpieces. It is mainly used for stamping parts of thin plate members with a thickness of less than 4mm and welding of steel bars. It has high production efficiency, With the characteristics of small deformation, high degree of automation, and rapid and concentrated energy release, it is especially suitable for the production and assembly of automobile body-in-white, welding of aircraft fuselage and other fields. During the spot welding process, the plate needs to be placed between the upper and lower electrode caps of the spot welding machine and clamped and energized,...
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IPC IPC(8): G06V10/764G06V10/774G06V10/82G06K9/62G06N3/04G06N3/08G06T3/40
CPCG06N3/08G06T3/4038G06T2200/32G06N3/045G06F18/2431G06F18/214
Inventor 王四宝焦健杨波王时龙王昱肖猛
Owner CHONGQING UNIV