Method for predicting shrinkage cavity defect of TC4 titanium alloy casting through BP neural network

Abstract

The invention belongs to a method for predicting a shrinkage cavity defect of a TC4 titanium alloy casting through a BP neural network. According to the method, the BP neural network is applied to shrinkage cavity position prediction of the TC4 titanium alloy casting; according to formation reasons of a shrinkage cavity and the characteristics of titanium alloy investment casting, the seven parameters of cavity filling time, material flowing distance, material age, free surface area*flowing time, local solidification time, cooling rate and temperature gradient are selected the first time to serve as input of the BP neural network; and by using the judgment of whether a corresponding position has a shrinkage cavity as output of the BP neural network, the method for effectively predicting the shrinkage cavity defect of the TC4 titanium alloy casting is established. Through the method, the position of the shrinkage cavity of TC4 titanium alloy in the casting process can be predicted moreaccurately, the difficulty in titanium alloy casting shrinkage cavity detection and the problem that prediction results of MAGMA, TSolver, ProCAST and other casting simulation software are insufficient in accuracy are solved, and a data basis is provided for precisely predicting and effectively avoiding the defect of the TC4 titanium alloy in the casting process.

Description

technical field [0001] The invention belongs to a method for predicting shrinkage cavity defects of TC4 titanium alloy castings by applying BP neural network. Background technique [0002] Titanium is an important structural metal developed in the 1950s. It not only has the superior performance of metal structural materials, but also has excellent corrosion resistance in many process media. It is the "third metal" after iron and aluminum. ", known as "space metal". Many countries in the world have recognized the importance of titanium alloy materials, researched and developed them one after another, and obtained practical applications. The composition of titanium alloy TC4 material is Ti-6Al-4V, which belongs to (α+β) type titanium alloy. It has good process plasticity, superplasticity, weldability and corrosion resistance, as well as good sheet formability and forging performance. Therefore, it is widely used in the aviation and aerospace industry, such as for the manufac...

AI Technical Summary

Problems solved by technology

Most of the existing numerical simulation methods of the casting process simplify the influencing factors. For example, the criteria for casting defects in the commonly used casting simulation software such as MAGMA, FTSolver and ProCAST only consider certain aspects such as the flow field and the temperature field. Influencing factors, but did not fully consider the two processes of filling and solidification of the casting, which more or less affects the analysis results, and the form of the discretization of the structure is different, the results and precision are also different, and the randomness is relatively large. reduce the accuracy of the forecast

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