Prediction of rolling force during isothermal rolling process based on machine learning

The Artificial neural networks (ANN) model established in this study can accurately predict the rolling force of Titanium-Aluminium (TiAl) alloy during isothermal rolling process, and can achieve online monitoring and rolling plan optimization. TiAl alloys are widely used in the aerospace field due to their high strength, low density, good corrosion resistance, and high temperature stability. However, the wrinkling and cracking problems of TiAl alloys are still difficult to solve. The important way to solve this problem is isothermal rolling, and the precise control of rolling force during the rolling process provides a strong guarantee for solving the problem. This study established a rolling force prediction model for TiAl alloy isothermal rolling based on genetic algorithm optimized Back Propagation (BP) neural network. The database used for training and testing is from a controlled two-roll rolling mill. The input parameters of the network are inlet temperature, roller temperature, rolling speed, reduction ratio and friction coefficient between roller and strip. The superiority of genetic algorithm is verified by comparing genetic algorithm with BP neural network optimized by fuzzy theory. The results show that when the population size is 40, the crossover probability is 0.2, the mutation probability is 0.1, the evolutionary algebra is 10, the transfer functions of the hidden layer and the output layer are tansig, and the structure of the neural network is 5-5-1, optimization works best. The maximum relative error is only 5.3%, the average relative error is only 1.6%, and the correlation coefficient is 99.78%.