Prediction of Final Rolling Temperature for TiAl Alloy Hot Rolling Based on Machine Learning

TL;DR

This paper introduces a machine learning model to accurately predict the final rolling temperature for TiAl alloys, improving control in the hot rolling process.

Contribution

A novel BP neural network model (GABP) based on a genetic algorithm is proposed for real-time prediction of final rolling temperature in TiAl alloy processing.

Findings

The GABP model's prediction error is mainly concentrated between 0–1 °C.

GABP outperforms fuzzy neural networks in predicting final rolling temperature for TiAl alloys.

MATLAB analysis identified key factors influencing final rolling temperature for neural network input.

Abstract

The final rolling temperature has a significant impact on the grain recrystallization and mechanical properties of rolled materials and is a key factor in the rolling process. With the development of the aerospace industry, higher requirements have been put forward for the quality of TiAl alloys. The suitable rolling temperature range of TiAl alloys is high and narrow, making it difficult to accurately control the final rolling temperature in real-time under the influence of environmental heat transfer and rolling heat. Finite element analysis can simulate the temperature fields, but takes a long time and is not suitable for online monitoring. Neural networks have the characteristic of fast response speeds and can be used for online control and rolling plan optimization. This article proposes a BP neural network prediction model (GABP) based on a genetic algorithm to predict the final rolling temperature. In order to determine the input parameters of the neural network, MATLAB was used to analyze the effects of various factors on the final rolling temperature. The prediction error of GABP is mainly concentrated at 0–1 °C. Compared with fuzzy neural networks (FNN), GABP has a higher prediction accuracy and can effectively predict the final rolling temperature of a TiAl alloy.

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