Titanium alloy constitutive relation prediction method based on machine learning

A constitutive relationship and machine learning technology, applied in the field of material science and metal material constitutive behavior prediction, can solve problems such as data ambiguity, unseen metal material prediction, failure to meet data fitting accuracy requirements, etc.

Active Publication Date: 2020-12-25
BEIJING INSTITUTE OF TECHNOLOGYGY +1
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Problems solved by technology

However, the data generated by the VAE model is often fuzzy and cannot meet the accuracy requirements of this stud...

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  • Titanium alloy constitutive relation prediction method based on machine learning
  • Titanium alloy constitutive relation prediction method based on machine learning
  • Titanium alloy constitutive relation prediction method based on machine learning

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Embodiment 1

[0057] The specific steps for predicting the constitutive relationship of titanium alloys based on machine learning are as follows:

[0058] S01-1: Obtain the real stress-strain curves of 13 kinds of titanium alloys under room temperature (25°C) quasi-static compression, high-temperature quasi-static compression and room temperature dynamic compression test conditions, and record them together with the experimental conditions, a total of 135 pieces of data; Among them, for the test data under repeated conditions, all quasi-static compressive stress-strain curves are retained, while for high-temperature quasi-static compressive or room temperature dynamic compressive stress-strain curves, only one curve is retained under the same temperature or strain rate conditions, all grades The temperature range of the high temperature quasi-static compression test is between 300°C and 550°C, and the strain rate of the dynamic compression test at room temperature is 1400s -1 ~4200s -1 Amo...

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Abstract

The invention relates to a titanium alloy constitutive relation prediction method based on machine learning, and belongs to the technical field of constitutive behavior prediction of metal materials.The prediction method comprises the following steps: obtaining and preprocessing stress-strain curves of various titanium alloys under different temperature and strain rate conditions; making a curvedata set which is independently used for training the a VAE-GAN model; constructing a prediction model part I based on the VAE-GAN model, and performing training; building a prediction model part II based on a polynomial regression model, and predicating coding of a stress-strain curve according to experimental conditions; and inputting the prediction codes into a VAE-GAN decoder, and outputting afinal prediction stress-strain curve. According to the prediction method, the change process and the failure strain of the stress of the titanium alloy material along with the strain are predicted atthe same time, the defect that a traditional constitutive model cannot predict the failure strain of the alloy material is overcome, and a new method is provided for alloy material constitutive relation prediction.

Description

technical field [0001] The invention relates to the application of machine learning technology in the field of material science, in particular to a method for predicting the constitutive relationship of various titanium alloys by using a neural network encoder model and a polynomial regression model, belonging to the technical field of constitutive behavior prediction of metal materials . Background technique [0002] Traditional empirical metal constitutive models, such as the Johnson-Cook model, can describe the constitutive behavior of metal materials at different strain rates and temperatures. However, due to the complex nonlinear relationship between the deformation of metal materials and the temperature and strain rate, the empirical model can only accurately fit the stress-strain curve within a limited range. Since the end of the last century, some scholars have tried to use the neural network (ANN) model to predict the constitutive behavior of materials. Since the n...

Claims

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Application Information

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IPC IPC(8): G16C10/00G16C60/00G16C20/70G06N3/04
CPCG16C10/00G16C60/00G16C20/70G06N3/047G06N3/045
Inventor 王扬卫赵平洛姜炳岳程兴旺牛海燕史辉
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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