Aluminum alloy micron-sized second phase quantitative statistical characterization method based on deep learning

Abstract

The invention discloses an aluminum alloy micron-sized second phase quantitative statistical characterization method based on deep learning, which comprises the following steps of: obtaining a featuredatabase of a standard sample, training the feature database by using a deep learning-based image segmentation network U-Net to obtain a U-Net segmentation model, and selecting parameters corresponding to optimal precision to establish a U-Net target model; cutting a to-be-tested aluminum alloy image, inputting the cut to-be-tested aluminum alloy image into the U-Net target model, obtaining size,area and position information of a second phase through a connected region algorithm, performing statistical distribution representation on the data set in combination with a mathematical statisticalmethod, and restoring the position information in the test image to the surface of the to-be-tested aluminum alloy to obtain a to-be-tested aluminum alloy surface, and obtaining a full-view-field quantitative statistical distribution condition and a visualization result. Based on a deep learning image segmentation algorithm, an aluminum alloy micron-scale second phase is automatically recognizedand extracted, extracted features are positioned and counted, and the method has the advantages of being large in view field, complete in information, accurate and reliable.

Description

technical field [0001] The present invention relates to the technical field of characterization of micron-scale second phases in aluminum alloys, in particular to a quantitative statistical characterization method for micron-scale second phases of aluminum alloys based on deep learning. Background technique [0002] Unmelted or refractory second-phase particles in aluminum alloys are generally produced during casting, and cannot be melted back in subsequent heat treatment and thermal deformation. These refractory secondary phases break up and elongate during deformation, and are arranged in a straight line along the deformation direction, consisting of short complementary connected strips. These particles are hard and brittle, and they are distributed inside the grains or on the grain boundaries. During plastic deformation, pores are easily formed on the phase interface, resulting in microcracks, which significantly reduce the fracture toughness of the material. In addition...

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Problems solved by technology

[0005] The purpose of the present invention is to provide a quantitative statistical characterization method for micron-level second phases of aluminum alloys based on deep learning, which uses high-throughput image data acquisition methods and deep learning algorithms to realize automatic identification of micron-level second phases of aluminum alloys, and combines The mathematical method excavates a variety of characterization parameters of the second phase, quantitatively counts the differences in the full field of view of the material surface and the distribution of partitions, and solves the problem of single quantitative characterization parameters of the second phase in traditional aluminum alloys. It has a large field of view, complete information, and accuracy. reliable features

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