The invention discloses an additive manufacturing
zirconium alloy magnetic susceptibility prediction method based on a
convolutional neural network, and belongs to the technical field of material
performance prediction and intelligent manufacturing. The method comprises the following steps: firstly, preparing a plurality of groups of
zirconium alloy samples with different process parameters through an
electron beam
powder bed melting process; secondly, performing X-
ray diffraction analysis on the sample to obtain normalized
diffraction intensity data of each
crystal face, and measuring the
magnetic susceptibility of each
crystal face; thirdly, constructing a
convolutional neural network model, and performing model training by taking the normalized
diffraction intensity data and the
phase composition data as input features and the
magnetic susceptibility as an output
label; and finally, predicting the
zirconium alloy magnetic susceptibility under unknown process parameters by using the trained model. According to the method, the CNN model reveals that the
crystal orientation is a leading factor influencing the magnetic susceptibility under the condition that the material components are the same, efficient and accurate prediction of the magnetic susceptibility of the
zirconium alloy is realized, and a new way is provided for design and optimization of an
MRI compatible implant material.