Welding simulator virtual weld defect detection method based on deep learning

Abstract

The invention discloses a welding simulator virtual weld defect detection method based on deep learning. A convolutional neural network CNN is used to realize virtual weld defect detection, the convolutional neural network CNN comprises an input layer, a convolutional layer, an excitation layer, a pooling layer and a full connection layer from top to bottom, an output value of an upper layer between two adjacent layers is used as an input value of a lower layer, and the invention belongs to the technical field of weld joint detection, and provides the welding simulator virtual weld joint defect detection method based on deep learning. The method comprises the following steps: firstly, on the basis of analyzing and collecting data characteristics, designing an identification algorithm of awelding seam image, performing graying processing and segmentation processing on the image, and remarkably separating a welding seam region from a non-welding seam region; secondly, constructing a deep learning network and expanding the collected data set; and finally, performing recognition training on the defect features by using a training learning framework.

Description

technical field [0001] The invention relates to the field of virtual weld defect detection of welding simulators, in particular to a method for detecting virtual weld defects of welding simulators based on deep learning. Background technique [0002] The level of welding technology is an important indicator to measure the strength of a large manufacturing country. The demand for welding workers in my country has always maintained a large trend, especially for high-tech welders. With the rapid development of virtual reality technology, welding training has gradually shifted from the traditional physical training mode to a new mode combining virtual simulation and physical training. The virtual welding simulator relies on its advantages of safety, efficiency, and pollution-free, and is widely used in welding training. However, how to evaluate the virtual welding quality and point out the students' operation problems in the computer virtual environment has always been a technic...

AI Technical Summary

Problems solved by technology

[0003] In the welding simulator, weld detection is often affected by the processing of two-dimensional images and detection methods, so it is impossible to effectively detect the specific defects in the weld or the identification of the weld takes too long, resulting in an overall decline in the quality of training. Some virtual welding simulators have low accuracy in identifying weld defects w

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