Abstract
In the foundry, the surface dry furnace is a special equipment for surface drying after sand core hydrophobic coating. In order to accurately predict whether it was possible to malfunction, four objective variables was used as input, and the health status of the equipment was used as the output. A prediction model based on the traditional BP neural network was established. This model combined genetic algorithm (GA) to optimize the initial weight of BP neural network; combined with LM (Levenberg-Marquardt) algorithm to improve the BP neural network, the error decreased too slowly when the predicted value approached the target value. Four kinds of evaluation methods were used in Matlab to compare the prediction results of the three models in simulation training. The research shows that the improved algorithm can overcome the problem that the traditional BP neural network has slow convergence rate and is easy to fall into the local optimal solution, and it has higher prediction accuracy, which provides a new solution to the fault prediction of the surface dry furnace.
Highlights
In the foundry, the working principle of the dry furnace is to generate heat through gas combustion, and uses the circulating fan to send the hot air of the combustion chamber into the furnace to spray and dry the workpiece[1]
Due to its own algorithm limitation, BP neural network still has some limitations, which are mainly reflected in two aspects: 1.Convergence is too slow. 2.Easy to fall into the local optimal solution
[2] proposed a general modeling method for machine tool thermal error based on genetic algorithm (GA) algorithm to optimize BP neural network structure and initial value
Summary
The working principle of the dry furnace is to generate heat through gas combustion, and uses the circulating fan to send the hot air of the combustion chamber into the furnace to spray and dry the workpiece[1]. [2] proposed a general modeling method for machine tool thermal error based on GA algorithm to optimize BP neural network structure and initial value. This method improves the prediction accuracy of neural network, but the problem of neural network convergence speed is not carried out to discuss in depth. [4] proposed a method combining GA algorithm and rough set to optimize neural network, which improved the prediction accuracy of traditional BP neural network and enhanced the generalization ability.
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