Microstructure Prediction Method of Cast Cylinder Head Based on Rough Set and Neural Network

Abstract

The invention relates to a method for predicting the microstructure of a cast cylinder head based on rough sets and neural networks, and belongs to the related field of cast aluminum alloy cylinder heads. The purpose of the present invention is to solve the problem of repeated trial and error in the existing casting process to determine the final reasonable process parameters. In order to reduce design and production costs, the casting process parameters can be optimized in the product design stage to reduce the metal consumption rate and castings. The purpose of scrap rate is to provide a method for predicting the microstructure of cast aluminum alloy cylinder head based on rough set and BP neural network; this method uses rough set theory to conduct various index attributes of casting and heat treatment processes that affect the microstructure of the material Reduction, the role of data reduction, on the one hand, selects indicators with larger weights, on the other hand, it reduces the input dimension of the neural network, enhances the efficiency of neural network learning, and improves the accuracy and efficiency of microstructure morphology prediction As a premise, the purpose of reducing the rate of metal consumables and casting scrap rate is finally achieved.

Description

technical field [0001] The invention relates to a method for predicting the microstructure of a cast cylinder head based on rough sets and neural networks, and belongs to the related field of cast aluminum alloy cylinder heads. Background technique [0002] In the field of cast aluminum alloy cylinder head, the casting and heat treatment process parameters of the cylinder head will affect its microstructure. If the casting and heat treatment process parameters are set reasonably, a dense microstructure can be obtained, thereby greatly reducing the metal consumption rate and casting rejection rate. There are many casting processing and heat treatment process parameters of cast aluminum alloy cylinder head, and it is difficult to describe the mapping relationship between the processing technology of cast aluminum alloy cylinder head and its microstructure morphology reasonably through simple analytical expressions. In the actual production process, the cylinder head mainly ad...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to solve the problem of repeated trial and error in the existing casting process to determine the final reasonable process parameters, in order to reduce design and production costs, optimize casting process parameters, and achieve the purpose of reducing the rate of metal consumables and casting rejects, and provides a A method for predicting microstructure of cast aluminum alloy cylinder head based on rough set and BP neural network; this method uses rough set theory to reduce various index attributes of casting and heat treatment processes that affect the microstructure of the material, and the role of data reduction On the one hand, it selects the indicators with larger weights, on the other hand, it reduces the input dimension of the neural network, enhances the learning efficiency of the neural network, and improves the accuracy and efficiency of microstructure morphology prediction, and finally achieves the reduction of metal consumables. rate and the purpose of casting reject rate

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