Artificial neural network predicting method of amorphous alloy thermoplasticity forming performance

Abstract

The invention belongs to the field of prediction of amorphous alloy thermoplasticity forming performance, and discloses an artificial neural network predicting method of the amorphous alloy thermoplasticity forming performance. The predicting method comprises the following steps of a, selecting multiple performance parameters and collecting data of the performance parameters, dividing the data into a training sample, a verification sample and a to-be-predicted sample, and testing to obtain feature index test values corresponding to the training sample and the verification sample; b, selectingan artificial neural network model as an initial predicting model for the amorphous alloy thermoplasticity forming performance, adopting the training sample to train the artificial neural network model, and determining an improved predicting model; c, adopting the verification sample to verify the improved predicting model, finally obtaining a final predicting model, and adopting the final predicting model for prediction. By means of the artificial neural network predicting method of the amorphous alloy thermoplasticity forming performance, the amorphous alloy thermoplasticity forming performance is effectively predicted without experiments, guidance is provided for development of an amorphous alloy system suitable for thermoplasticity forming, the time for developing the new amorphous alloy system is greatly shortened, and the money cost for developing of the new amorphous alloy system is greatly reduced.

Description

technical field [0001] The invention belongs to the field of thermoplastic forming performance prediction of amorphous alloys, and more specifically relates to an artificial neural network prediction method for thermoplastic forming performance of amorphous alloys. Background technique [0002] Amorphous alloy has a very large elastic limit and high fracture strength at room temperature, but its room temperature plasticity is low, so it is difficult to use conventional plastic processing methods for forming and manufacturing at room temperature, which restricts amorphous alloys to a certain extent. The application of alloys, however, due to the amorphous structure similar to common oxide glasses, amorphous alloys also have glass transition and softening rheology like inorganic glass, making them in the supercooled liquidus temperature region (glass transition temperature T g and onset crystallization temperature T x The temperature range between) has superplastic forming p...

AI Technical Summary

Problems solved by technology

[0008] Aiming at the above defects or improvement needs of the prior art, the present invention provides an artificial neural network prediction method for the thermoplastic formability of amorphous alloys. The final prediction model is established by using the BP artificial neural network model and combining the genetic algorithm. The purpose is to predict the thermoplastic forming properties of amorphous alloys, thereby solving the technical problems of difficulty in predicting the thermoplastic forming properties of amorphous alloys and high cost

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