Full convolutional neural network and corresponding microstructure identification method

Abstract

The invention relates to a full convolutional neural network and a corresponding microstructure identification method, by comprehensively adopting a maximum pooling index connection method and a channel connection method in the network, serial processing of the two methods is carried out, shallow low-level edge information of an encoder and deep semantic information of a decoder are fully utilized, and a better effect is achieved. In the process of amplifying the image resolution, only an up-sampling mode is adopted, so that the consumption of a video memory is reduced, and the time of networktraining is also reduced. Batch regularization is adopted through cross symmetry, the original distribution state of the feature map after convolution is ensured as much as possible, the expression ability of the convolutional neural network is further enhanced, semantic segmentation of CMCs preform XCT slices is achieved, and the accuracy of microstructure recognition is improved. The same fullconvolutional neural network is adopted to carry out microstructure identification on braided structures of different ceramic matrix composite material complex preforms, different full convolutional neural network weight files adopted by different braided structures are abandoned, and identification operation is simplified.

Description

technical field [0001] The invention belongs to the field of mesoscopic structure recognition of prefabricated ceramic matrix composite materials, and in particular relates to a fully convolutional neural network for semantic segmentation of XCT slices of woven ceramic matrix composite materials. Background technique [0002] Ceramic matrix composites (CMCs, Ceramic Material Composites) usually use continuous ceramic fibers as the reinforcement phase, which is a material that can be applied under extreme conditions, and has the advantages of high temperature resistance, corrosion resistance, low density, and wear resistance. [0003] The skeleton composed of continuous ceramic fibers is called a complex preform, and its weaving structure includes plain weave, 2.5D, three-dimensional four-way, etc. The preparation process of CMCs includes chemical vapor deposition (CVI), precursor impregnation cracking (PIP), reaction melting (MI) and other methods. Usually, CMCs are prepare...

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Problems solved by technology

[0012] Aiming at the deficiencies in the prior art, the present invention provides a fully convolutional neural network and a corresponding mesostructure recognition method for the mesostructure recognition of XCT slices of ceramic matrix composite material preforms, because it is applied in the field of CMC, so Called CMCs_Net, it solves the problem of large memory usage and long training time of the full convolutional neural network, and further improves the accuracy of the network

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