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Method for predicting TC6 titanium alloy forging piece microstructural parameters

A technology of microstructure and prediction method, applied in geometric CAD, electrical digital data processing, special data processing applications, etc., can solve problems such as poor practicability, and achieve high accuracy and reliability

Inactive Publication Date: 2016-06-15
NORTHWESTERN POLYTECHNICAL UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] In order to overcome the shortcomings of poor practicability of the existing methods for predicting the microstructure parameters of forgings, the present invention provides a method for predicting the microstructure parameters of TC6 titanium alloy forgings

Method used

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  • Method for predicting TC6 titanium alloy forging piece microstructural parameters
  • Method for predicting TC6 titanium alloy forging piece microstructural parameters
  • Method for predicting TC6 titanium alloy forging piece microstructural parameters

Examples

Experimental program
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Effect test

Embodiment 1

[0034] Example 1: Prediction of primary α-phase grain size of TC6 titanium alloy forgings.

[0035] (1) Machining the supplied TC6 titanium alloy bar with a diameter of 35mm to obtain a thermal simulation compression sample with a diameter of 8mm and a height of 12mm;

[0036] (2) Clean the TC6 titanium alloy sample with absolute ethanol, and coat the cleaned surface with glass lubricant;

[0037] (3) Select forging deformation temperature 1073K, 1103K, 1133K, 1163K, 1193K, 1223K, 1253K, 1283K, 1313K, strain rate 0.001, 0.01, 1, 10, 50s -1 , The maximum strain is 0.69; the TC6 titanium alloy is placed in the center of the Thermocmaster-Z thermal simulation compression testing machine workbench, and the compression deformation experiment is performed on the TC6 titanium alloy after the temperature is kept for 5 minutes. After the compression deformation experiment, it is quickly cooled to room temperature by blowing nitrogen;

[0038] (4) Take half of the sample along the axis after the...

Embodiment 2

[0072] Example 2: Prediction of the volume fraction of primary α phase of TC6 titanium alloy forgings.

[0073] The difference between the implementation steps of Example 1 is that the volume fraction is measured in step (4), and the volume fraction (f α ,%) is the output value of the function;

[0074] The prediction model for the volume fraction of primary α phase of TC6 titanium alloy forgings is,

[0075]

[0076] A sample of teachers who selected the volume fraction of the primary α phase of TC6 titanium alloy is shown in Table 3. Use teacher samples to train the volume fraction prediction model formula (14). When the cumulative error of the volume fraction is less than 2%, the fuzzy rule weight coefficient is optimized And fuzzy rule weight (w i ).

[0077] Substituting the determined weight coefficient and weight value of the fuzzy rule into equation (14), it is the prediction model of the volume fraction of primary α phase of TC6 titanium alloy forging. The predicted value o...

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Abstract

The invention discloses a method for predicting TC6 titanium alloy forging piece microstructural parameters, aimed at addressing the technical problem of poor practicality of current methods for predicting forging piece microstructural parameters. The technical solution includes the following steps: firstly determining TC6 titanium alloy forging deformation temperature, strain rate and 9 membership functions of straining and 27 fuzzy rules, then establishing a mathematical model between TC6 titanium primary [alpha] phase crystal grain size and volume fraction; adopting TC6 titanium alloy high temperature compression deformation experiment data, optimizing weight coefficient and weight value of the fuzzy rules, substituting the optimized weight coefficient and weight value to the mathematical model between TC6 titanium primary [alpha] phase crystal grain size and volume fraction, obtaining a prediction model between TC6 titanium primary [alpha] phase crystal grain size and volume fraction. According to the invention, the maximum error between a predicted testing sample result between TC6 titanium alloy forging piece crystal grain size and volume fraction and an experimental result is less than 5 %. The method of the invention has higher accuracy and strong practicality.

Description

Technical field [0001] The invention relates to a method for predicting microstructure parameters of forgings, in particular to a method for predicting microstructure parameters of TC6 titanium alloy forgings. Background technique [0002] In the field of titanium alloy forging deformation technology, while ensuring the shape and dimensional accuracy of forgings, more attention is paid to the mechanical properties of forgings. The mechanical properties of forgings are directly determined by the microstructure of forgings. Therefore, under the premise of not changing the material composition, by optimizing the process parameters to reasonably and effectively control the forging process, the microstructure and mechanical properties that meet the design requirements can be obtained. In particular, difficult-to-deform metal materials represented by titanium alloys are generally used to manufacture key forgings, and it is necessary to obtain a good microstructure to ensure the mechan...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50
CPCG06F30/17G06F2119/18
Inventor 李淼泉林海熊爱明
Owner NORTHWESTERN POLYTECHNICAL UNIV
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