Prediction Of Heat Treatment Performance And Intelligent Optimization Of Process Parameter Of Aluminum Alloy Sheet And Strip

In recent years,the production equipment level of aluminum alloy plate and strip in China has developed rapidly,which has reached the advanced level in the world.However,compared with foreign countries,there is still a big gap in the production control of high-level aluminum alloy plate and strip.The quality and performance stability of high-level products is one of them.In this paper,the aluminum alloy sheet and strip heat treatment production is taken as the research object,and the intelligent control method of heat treatment process performance and process is studied with the aid of artificial intelligence technology,the aluminum alloy performance prediction model based on deep learning is established,and the intelligent optimization method of heat treatment process parameters is studied based on this.The main research contents are as follows:(1)Based on in-depth understanding of the production process of aluminum alloy sheet and strip,this paper studies the heat treatment performance of aluminum alloy sheet and its influencing factors,analyzes the process parameters affecting the performance of products after air cushion heat treatment from the aspects of physical metallurgy knowledge and data analysis,and provides the data basis for subsequent research;This paper also studies and analyzes the types and modeling methods of deep learning neural network,so as to provide theoretical basis for the establishment of performance prediction model.(2)In view of the multi-variable and non-linear characteristics of the production data in the process of aluminum alloy heat treatment production,as well as the defect that the accuracy of traditional prediction technology model is difficult to reach the ideal degree,this paper introduces the deep feedforward neural network model to study the performance prediction of heat treatment on aluminum alloy sheet and strip,and establishes the data processing method and performance prediction model for tensile strength,yield strength and elongation after fracture,analyzes the influence of hidden layer number,hidden layer node number,optimization function,activation function and normalization method on the model accuracy,and determines the optimal performance prediction model structure and internal parameters.(3)In view of the multi-dimensional optimization objective of the heat treatment process of aluminum alloy sheet and strip,the genetic algorithm is introduced to establish the optimization strategy of process parameters for the optimization of performance,production efficiency and energy consumption,and a two-step evaluation method is proposed.The influence of the model parameters on the calculation speed and accuracy is analyzed,and the heat treatment performance prediction model is established on the MATLAB platform.Through the model,the intelligent optimization solution method of process parameters based on genetic algorithm is built,and determine the optimal optimization model algorithm and parameters of heat treatment process parameters,so as to obtain the heat treatment process parameters that meet the requirements of the target.