Research On Temperature-Structure Coupling Model And Solidification Control System Of Aluminum Alloy Casting Process

The foundry industry is the key building stone to ensure the good operation of China's economy.As an upstream industry of most enterprises,it provides very important support for China's economic development,especially in the automotive industry.Many of cars' components are cast and aluminum is produced.The alloy engine cylinder head is the main part of it.For the key cooling and solidification process in the casting process,the gravity casting system still relies on the backward manual operation in the temperature control strategy.The product quality is difficult to be guaranteed,and inconsistent problems of the product quality often occurs.Based on the background of this research,combined with the existing research basis,the influencing factors of temperature control in gravity casting process is analysed to construct the coupling model of temperature structure in solidification process,and reas the optimal temperature control performance of solidification process under different control strategies.In order to improve the consistency of mechanical properties and quality of aluminum alloy casting products,it has important economic and social significance.The main research contents are as follows:Firstly,the temperature structure coupling model of gravity casting solidification process of aluminum alloy engine cylinder head is constructed.The process flow of gravity casting is analyzed,and the thermal nodes and water cooling point arrangement rules are determined.Based on this,the temperature field model is constructed.The interface heat transfer coefficient is the key index that affects the modeling accuracy of the solidification process.Therefore,the influence of casting structure on temperature field is needed.The nonlinear estimation method is used to calculate the relationship of interface heat transfer coefficient with time,so as to complete the construction of temperature structure coupling model during solidification.Then,based on the temperature-structure coupling model,the temperature control strategy of the solidification process is studied.The temperature control of the solidification process has strong nonlinearity,strong coupling and uncertainty.According to the characteristics of uncertainty conversion between qualitative and quantitative,the method which combined cloud model and PID are proposed.The principle and digital characteristics of the cloud model are utilized to build different cloud model rules.A set of solidification process temperature control system was designed and simulated by Matlab.The simulation results show that the cloud model is superior to the traditional algorithm in control effect,but there is still room for optimization.Next,the cloud model PID controller can not quickly respond to nonlinear physical changes in castings,so control strategy optimization method is studied.The neural network is used to fit the control parameters,and the neural network-based cloud model control strategy is analyzed.The system can dynamically adjust the cloud model membership function output,so as to continuously fit the actual physical condition of the casting and realize the rapid response system change.The simulation analysis of the system,combined with the characteristics of gravity casting solidification process,optimizes the parameters of the neural network and compares it with other control strategies.The results show that the control performance of the cloud model PID control strategy based on neural network has been further improved.Finally,three different strategies are experimentally verified and analyzed.First,establish the software and hardware design of the experimental platform,and set the experimental material parameters.Then,for the temperature measurement system of the experimental system,the nonlinear temperature regression is used to compensate the PLC temperature measurement value so that its height is close to the true value.Finally,three different control strategies are used in the three working points of the mold for experimental analysis and comparison.Through the error analysis of the respective temperature curves and the mechanical properties of the final castings,it is shown that the neural network-based cloud model PID has the gravity casting solidification process.Higher stability and better followability with better mechanical properties.