CN113823359A - Method for optimizing casting cooling process parameters of aluminum alloy steering gear valve shell - Google Patents
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Abstract
The invention discloses a method for optimizing casting and cooling process parameters of an aluminum alloy steering gear valve shell, which comprises the following steps of: (1) establishing a pouring system numerical model and a defect prediction model in the casting molding process of the valve shell of the aluminum alloy diverter to be optimized; (2) selecting a plurality of cooling system process parameters as optimization variables, determining a design space and an optimization target, and extracting test sample points by adopting a Latin hypercube test design method; (3) carrying out analog simulation by substituting the test design sample into a numerical model to obtain a target value of the defect volume of the steering gear valve shell, establishing a nonlinear approximate model reflecting the relation between input and output by adopting a kriging interpolation method, and evaluating the reliability of the model; (4) and obtaining a cooling process parameter combination of the diverter valve shell with the minimum defect volume according to the Krigin model, bringing the optimization result into a pouring system numerical model and a defect prediction model for analysis, and verifying the process parameters of the cooling system until the optimal process parameters are obtained.
Description
Technical Field
The invention belongs to the technical field of high-strength metal forming, particularly relates to an aluminum alloy forming technology, and particularly relates to a method for optimizing casting cooling process parameters of an aluminum alloy steering gear valve shell.
Background
The steering gear valve shell is a part of a steering gear, has important functions of protecting and fixing a rack and an input shaft, limits the stroke of the rack, and directly influences the hydraulic characteristics of a steering gear assembly by the positions of an oil outlet hole and an oil inlet hole on the steering gear valve shell, thereby being related to the driving safety of an automobile. At present, with the gradual increase of national requirements on energy conservation and emission reduction, more and more enterprises increase the investment on the light-weight technology of automobile parts, and a steering gear valve shell is used as a part of chassis parts, and the traditional cast iron material is gradually replaced by aluminum alloy with lighter weight and better performance. With new materials and new casting processes, the quality of the diverter valve housing is not satisfactory, and the product reject rate of some plants approaches 20%. Therefore, there is a need to provide a practical and effective method for reducing the casting defects of the diverter valve housing to improve the product quality.
In the process of casting and forming the steering gear valve shell, the cooling effect of the cooling water has an important influence on the temperature distribution field and the forming quality of the casting. Therefore, the casting defects of the diverter valve shell can be reduced and the quality can be improved by optimizing the cooling process parameters (including cooling water flow, temperature, time, pressure, water flow density and the like) of the cooling system. The traditional optimization design method for the parameters mainly determines the optimal solution range of the optimized parameters through single-factor tests and selects the optimal solution range through the experience of production personnel, and the method is difficult to find out specific numerical values of the optimized parameters and cannot meet the actual production requirements. Therefore, the invention provides a method for optimizing casting cooling process parameters of an aluminum alloy steering gear valve shell, which solves the problem of determining the accurate value of the optimal cooling process parameters.
Disclosure of Invention
The invention aims to solve the problems that the optimal solution range of the optimized parameters is determined mainly through a single-factor test and the specific numerical values of the optimized parameters are difficult to find through experience selection of production personnel and the actual production requirements cannot be met by the conventional optimization design method of the cooling process parameters in the casting and forming process of the aluminum alloy steering gear valve shell.
The technical scheme of the invention is as follows: a method for optimizing casting cooling process parameters of an aluminum alloy steering gear valve shell comprises the following steps:
(1) establishing a pouring system numerical model and a defect prediction model in the casting molding process of the valve shell of the aluminum alloy diverter to be optimized;
(2) selecting a plurality of cooling system process parameters as optimization variables, determining a design space and an optimization target, and extracting test sample points by adopting a Latin hypercube test design method;
(3) carrying out analog simulation by substituting the test design sample into the pouring system numerical model in the step (1) to obtain a target value of the defect volume of the steering gear valve shell, establishing a nonlinear approximate model reflecting the relation between input and output by adopting a kriging interpolation method, and evaluating the reliability of the model;
(4) and (4) optimizing the Kriging model established in the step (3) by adopting a self-adaptive simulated annealing algorithm to obtain a steering gear valve shell cooling process parameter combination with the minimum defect volume, bringing the optimization result into a pouring system numerical model and a defect prediction model for analysis, and verifying the cooling system process parameters until the optimal process parameters are obtained.
Further, the step (1) of establishing a gating system numerical model for optimizing the object casting molding process comprises the following steps:
1) establishing a pouring system structure including a pouring channel, a casting, a core, a cooling pipeline and the like in equal proportion according to actual production conditions, determining pouring process parameters, carrying out meshing on a structural model by using finite element software, and setting materials and properties thereof;
2, when the temperature distribution condition of molten metal in the filling and solidifying processes is calculated, considering that the molten metal is cooled from a high-temperature molten state to be solid, the temperature of the molten metal is continuously changed, and the thermophysical property of the aluminum alloy is changed along with the change of the temperature, so that thermophysical parameters such as the heat conductivity coefficient, the density, the solid phase ratio, the specific heat capacity and the like of the aluminum alloy cannot be set to be fixed values and are set to be changed along with the temperature according to a corresponding rule so as to ensure the precision of the model;
3) considering that heat exchange exists in a pouring system model in the casting process, and the difference of heat exchange coefficients of different materials at different temperatures is large, the heat exchange coefficient is set within a reasonable range according to the actual production condition, and particularly, the heat exchange coefficient which changes along with time is adopted between molten metal and a casting mold due to the influence of spraying paint and the thickness of a coating.
Further, the step (1) of establishing a defect prediction model of the diverter valve housing needs to be combined with a numerical model of a pouring system, and a solidification process diagram, a temperature distribution diagram, a solid fraction diagram and the like are added to determine the time, the position and the size of the defect.
Further, the optimization variables of the step (2) are combined with the production site conditions of the diverter valve housing and the controllable cooling system process parameters in the casting process.
Further, the design space of the optimization variables in step (2) refers to a parameter variation range including the optimal cooling process parameters.
Further, the optimization target of the step (2) is that the volume of shrinkage cavity defects of the steering valve shell is minimum after the solidification process.
Further, the step (2) of extracting the test sample points by adopting the latin hypercube test design means that D dimensions of the sample space are equally divided into n intervals, n is the number of samples, and only one sample point is randomly selected for each interval in each dimension, so that the obtained n sample points have the projection uniformity of each dimension.
Further, the step (3) of establishing the nonlinear approximation model of the reaction input and output relationship by using the kriging interpolation method refers to the combination of a global approximation model and a local deviation model, and the expression is as follows:
Y(x)＝g(x)+Z(x)
wherein Y (x) is a response function representing the predicted value of the Krigin model; g (x) is a polynomial function representing the global trend based on the sample space, and z (x) is a random bias function representing the local bias of the model. In the case of N samples and an input value of x: the expression of the approximate estimate of the kriging model for the observed sample point is as follows:
in the formula: y is an N-dimensional column vector, and each element in the vector corresponds to the observation response value of N sample points respectively; f is an N-dimensional unit column vector; r (x) is an N-dimensional column vector representing the correlation between the input parameter x and the set of sample points,
the value of (A) can be calculated by a generalized least squares estimation method, the calculation formula is as follows
Further, the adaptive simulated annealing algorithm in the step (4) is to add an adaptive weight w for solving the problem that the optimization precision and the convergence speed of the simulated annealing algorithm cannot be considered at the same time, and is characterized in that the attenuation at the initial stage is fast, the attenuation at the later stage is gradually slow, and the weight mechanism can improve the stability of convergence.
And further, in the step (4), a cooling process parameter combination with the minimum defect volume of the steering gear valve shell is obtained through the kriging model constructed in the step (3), the optimization result is brought into the numerical model of the gating system and the defect prediction model for analysis, the defect volume analysis results before and after the cooling process parameter optimization are compared, if the optimized cooling process parameter can obviously reduce the defect volume of the casting, the requirement is met, the optimization is finished and the optimization result is output, otherwise, the prediction model is reconstructed and the optimization is continued.
Has the advantages that: according to the invention, the nonlinear finite element simulation analysis and the Krigin test model of the aluminum alloy steering gear valve shell in the casting and forming process are combined, and the adaptive simulated annealing algorithm is adopted for parameter optimization, so that the optimization efficiency and precision of cooling process parameters are improved, the defect volume of the steering gear valve shell can be reduced through the optimization design of the cooling system process parameters, the quality qualification rate is improved, and the production efficiency and the economic benefit are improved. The method solves the problems that the accuracy and the optimization of the result are difficult to ensure in the prior optimization technology, and has higher reliability, applicability and operability.
Drawings
FIG. 1 is a flow chart of the cooling process parameter optimization design technique of the present invention.
FIG. 2 is a schematic drawing of the casting process of the aluminum alloy diverter valve housing of the present invention.
FIG. 3 is a diagram of the fitting of the predicted value and the actual value of the Kriging model of the present invention.
FIG. 4 is a basic flow diagram of the adaptive simulated annealing algorithm of the present invention.
Detailed Description
The invention is further described with reference to the following figures and detailed description.
A method for optimizing casting cooling process parameters of an aluminum alloy diverter valve shell is shown in a flow chart of fig. 1 and comprises the following steps:
1. establishing a pouring system numerical model and a defect prediction model in the casting molding process of the valve shell of the aluminum alloy diverter to be optimized;
the method for establishing the gating system numerical model of the optimized object casting molding process comprises the following steps:
(1) establishing a pouring system structure according to an equal proportion according to actual production conditions, as shown in figure 2, wherein the pouring system structure comprises a pouring channel 1, a casting 2, a core 3, a cooling pipeline 4 and the like, determining pouring process parameters, carrying out meshing on a structure model by using finite element software, and setting materials and properties thereof;
(2) when the temperature distribution condition of molten metal in the filling and solidifying processes is calculated, the temperature of the molten metal is constantly changed when the molten metal is cooled from a high-temperature molten state to a solid state, and the thermophysical property of the aluminum alloy is changed along with the change of the temperature, so that thermophysical parameters such as the heat conductivity coefficient, the density, the solid phase ratio, the specific heat capacity and the like of the aluminum alloy cannot be set to be fixed values and are set to be changed along with the temperature according to a corresponding rule so as to ensure the precision of a model;
(3) considering that heat exchange exists in a pouring system model in the casting process, and the difference of heat exchange coefficients of different materials at different temperatures is large, the heat exchange coefficient is set within a reasonable range according to the actual production condition, and particularly, the heat exchange coefficient which changes along with time is adopted between molten metal and a casting mold due to the influence of spraying paint and the thickness of a coating.
A defect prediction model of a diverter valve shell is established, a solidification process diagram, a temperature distribution diagram, a solid fraction diagram and the like are added in combination with a pouring system numerical model, so that the time, the position and the size of defect generation are determined.
2. Selecting a plurality of cooling system process parameters as optimization variables, determining a design space and an optimization target, and extracting test sample points by adopting a Latin hypercube test design method;
in the invention, the cooling water flow, the cooling water temperature and the cooling time which can be adjusted in the casting cooling system of the aluminum alloy diverter valve shell of a certain casting enterprise are taken as optimization variables. It is understood that other cooling process parameters than the three can be selected as optimization variables, and the present invention is only exemplary and not limiting to the present invention in terms of cooling water temperature, cooling water flow and cooling time.
The design space in the invention refers to the variation range of cooling process parameters as optimization variables, the cooling water flow, the cooling water temperature and the cooling time are selected as investigation factors, the defect volume of the diverter valve shell after the solidification process is finished is taken as an evaluation index, and a single-factor test is carried out. The design space for each optimization variable is determined as shown in table 1.
TABLE 1 design space of optimized variables
The optimization target of the optimization variable in the invention means that the shrinkage cavity defect volume of the steering gear valve shell is minimum after the solidification process is finished.
The method comprises the steps of (1) extracting test sample points by adopting a Latin hypercube test design, wherein the Latin hypercube test design has three optimization design variables, the dimension D is 3, and the sample number n is 9, so that only one sample point is randomly selected for each interval in each dimension, and the obtained sample points have the projection uniformity of each dimension;
3. carrying out analog simulation by substituting the test design sample into the numerical model of the pouring system in the step 1 to obtain a target value y of the defect volume of the steering gear valve shell, wherein the result is shown in a table 2, a nonlinear approximate model for reflecting the relation between input and output is established by adopting a Krigin interpolation method, and the reliability of the model is evaluated;
TABLE 2 test sample points and target values thereof
The nonlinear approximation model for establishing the relation between the input and the output by adopting a kriging interpolation method is the combination of a global approximation model and a local deviation model, and the expression is as follows:
Y(x)＝g(x)+Z(x)
wherein Y (x) is a response function representing the predicted value of the Krigin model; g (x) is a polynomial function representing the global trend based on the sample space, and z (x) is a random bias function representing the local bias of the model.
In the case of N samples and an input value of x: the expression of the approximate estimate of the kriging model for the observed sample point is as follows:
in the formula: y is an N-dimensional column vector, and each element in the vector corresponds to the observation response value of N sample points respectively; f is an N-dimensional unit column vector; r (x) is an N-dimensional column vector representing the correlation between the input parameter x and the set of sample points,
the value of (A) can be calculated by a generalized least squares estimation method, the calculation formula is as follows
The reliability of the model is evaluated, the fitting condition of the predicted value and the actual value is shown in figure 3, the root mean square error is calculated to be 0.04453, the degree of deviation of the measured data from the true value is reflected, the smaller the value is, the higher the precision is, therefore, the fitting model meets the reliability requirement, and the target value prediction can be well carried out.
4. And (4) optimizing the Kriging model established in the step (3) by adopting a self-adaptive simulated annealing algorithm to obtain a steering gear valve shell cooling process parameter combination with the minimum defect volume, bringing the optimization result into a pouring system numerical model and a defect prediction model for analysis, and verifying the cooling system process parameters until the optimal process parameters are obtained.
The self-adaptive simulated annealing algorithm aims at the problem that the optimizing precision and the convergence speed of the simulated annealing algorithm cannot be considered at the same time, and is added with a self-adaptive weight w, and is characterized in that the attenuation at the initial stage is relatively fast, the attenuation at the later stage is gradually slow, the weight mechanism can improve the stability of convergence, and the basic flow chart of the algorithm is shown in fig. 4.
In order to verify the result obtained by optimization, a numerical model and a defect prediction model of the gating system are reestablished, the analysis results of the defect volume of the valve shell of the aluminum alloy steering gear before and after the cooling process parameters are optimized are compared, if the optimized cooling process parameters can obviously reduce the defect volume, the requirements are met, the optimization is finished, the optimization result is output, and otherwise, the prediction model is reestablished and the optimization is continued.
Before optimization, the temperature of cooling water in a cooling system in the actual production process is 25 ℃, and the flow is 1m3The cooling time is 60s, and the casting defect volume is 0.268cm3. After optimization, the temperature of the cooling water is 26 ℃, and the flow rate is 1.4m3The cooling time is 44s, the casting defect volume is 0.193cm3, the reduction is 28 percent, and the quality is greatly improved.
The foregoing is illustrative of the preferred embodiments of the present invention only and is not to be construed as limiting the claims. The invention is not limited to the above examples, which allow for variations in the specific optimization variables. In general, all changes which come within the scope of the invention as defined by the independent claims are intended to be embraced therein.
The parts not involved in the present invention are the same as or can be implemented using the prior art.
Claims (10)
1. A parameter optimization method for a casting cooling system of an aluminum alloy steering gear valve shell is characterized by comprising the following steps:
(1) establishing a pouring system numerical model and a defect prediction model in the casting molding process of the valve shell of the aluminum alloy diverter to be optimized;
(2) selecting a plurality of cooling system process parameters as optimization variables, determining a design space and an optimization target, and extracting test sample points by adopting a Latin hypercube test design method;
(3) carrying out analog simulation by substituting the test design sample into the pouring system numerical model in the step (1) to obtain a target value of the defect volume of the steering gear valve shell, establishing a nonlinear approximate model reflecting the relation between input and output by adopting a kriging interpolation method, and evaluating the reliability of the model;
(4) and (4) optimizing the Kriging model established in the step (3) by adopting a self-adaptive simulated annealing algorithm to obtain a steering gear valve shell cooling process parameter combination with the minimum defect volume, bringing the optimization result into a pouring system numerical model and a defect prediction model for analysis, and verifying the cooling system process parameters until the optimal process parameters are obtained.
2. The method for optimizing the parameters of the casting cooling system of the aluminum alloy steering gear valve shell according to claim 1, wherein the method comprises the following steps: the method comprises the following steps of (1) establishing a gating system numerical model in the casting molding process of the valve shell of the aluminum alloy diverter of the optimized object, wherein the gating system numerical model comprises the following steps:
(1) establishing a pouring system structure according to actual production conditions in equal proportion, wherein the pouring system structure comprises a pouring channel, a casting, a core and a cooling pipeline, determining pouring process parameters, carrying out meshing on a structural model by using finite element software, and setting materials and properties thereof;
(2) when the temperature distribution condition of molten metal in the filling and solidifying processes is calculated, the temperature of the molten metal is constantly changed when the molten metal is cooled from a high-temperature molten state to a solid state, and the thermophysical property of the aluminum alloy is changed along with the change of the temperature, so that thermophysical parameters such as the heat conductivity coefficient, the density, the solid phase ratio, the specific heat capacity and the like of the aluminum alloy cannot be set to be fixed values and are set to be changed along with the temperature according to a corresponding rule so as to ensure the precision of a model;
(3) considering that heat exchange exists in a pouring system model in the casting process, and the difference of heat exchange coefficients of different materials at different temperatures is large, the heat exchange coefficient is set within a reasonable range according to the actual production condition, and particularly, the heat exchange coefficient which changes along with time is adopted between molten metal and a casting mold due to the influence of spraying paint and the thickness of a coating.
3. The method for optimizing the parameters of the casting cooling system of the aluminum alloy steering gear valve shell according to claim 1, wherein the method comprises the following steps: and (2) establishing a defect prediction model of the diverter valve shell in the step (1), and adding a solidification process diagram, a temperature distribution diagram and a solid fraction diagram to determine the time, the position and the size of the defect generation by combining a pouring system numerical model.
4. The method for optimizing the parameters of the casting cooling system of the aluminum alloy steering gear valve shell according to claim 1, wherein the method comprises the following steps: and (3) optimizing variables in the step (2) by combining the production site conditions of the diverter valve housing and controllable cooling system process parameters in the casting process.
5. The method for optimizing the parameters of the casting cooling system of the aluminum alloy steering gear valve shell according to claim 1, wherein the method comprises the following steps: the design space of the optimization variables in the step (2) refers to a parameter variation range containing the optimal cooling process parameters.
6. The method for optimizing the parameters of the casting cooling system of the aluminum alloy steering gear valve shell according to claim 1, wherein the method comprises the following steps: the optimization target of the step (2) is that the volume of the shrinkage cavity defect of the steering gear valve shell is minimum after the solidification process.
7. The method for optimizing the parameters of the casting cooling system of the aluminum alloy steering gear valve shell according to claim 1, wherein the method comprises the following steps: the step (2) of extracting the test sample points by adopting the Latin hypercube test design means that D dimensions of a sample space are equally divided into n intervals, n is the number of samples, and only one sample point is randomly selected from each interval in each dimension, so that the obtained n sample points have the projection uniformity of each dimension.
8. The method for optimizing the parameters of the casting cooling system of the aluminum alloy steering gear valve shell according to claim 1, wherein the method comprises the following steps: the step (3) of establishing the nonlinear approximation model reflecting the relation between the input and the output by adopting a kriging interpolation method refers to the combination of a global approximation model and a local deviation model, and the expression is as follows:
Y(x)＝g(x)+Z(x)
wherein Y (x) is a response function representing the predicted value of the Krigin model; g (x) is a polynomial function representing the global trend based on the sample space, and z (x) is a random bias function representing the local bias of the model; in the case of N samples and an input value of x: the expression of the approximate estimate of the kriging model for the observed sample point is as follows:
in the formula: y is an N-dimensional column vector, and each element in the vector corresponds to the observation response value of N sample points respectively; f is an N-dimensional unit column vector; r (x) is an N-dimensional column vector representing the correlation between the input parameter x and the set of sample points,
the value of (A) is calculated by a generalized least squares estimation method, the calculation formula is as follows
9. The method for optimizing the parameters of the casting cooling system of the aluminum alloy steering gear valve shell according to claim 1, wherein the method comprises the following steps: the self-adaptive simulated annealing algorithm in the step (4) aims at the problem that the optimization precision and the convergence speed of the simulated annealing algorithm cannot be considered at the same time, and the self-adaptive weight w is added, and is characterized in that the attenuation is fast in the initial stage and gradually slow in the later stage, and the weight mechanism can improve the stability of convergence.
10. The method for optimizing the parameters of the casting cooling system of the aluminum alloy steering gear valve shell according to claim 1, wherein the method comprises the following steps: and (4) obtaining a cooling process parameter combination with the minimum defect volume of the steering gear valve shell through the Krigin model constructed in the step (3), bringing an optimized result into a gating system numerical model and a defect prediction model for analysis, comparing defect volume analysis results before and after optimization of the cooling process parameters, meeting requirements if the optimized cooling process parameters can obviously reduce the defect volume of a casting, finishing optimization and outputting an optimized result, and otherwise, reconstructing the prediction model and continuing optimization.
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| CN116994682A (en) * | 2023-08-01 | 2023-11-03 | 佛山市蓝宇机械设备有限公司 | Control method and system of aluminum alloy smelting and casting integrated equipment |
| CN116994682B (en) * | 2023-08-01 | 2024-01-23 | 佛山市蓝宇机械设备有限公司 | Control method and system of aluminum alloy smelting and casting integrated equipment |
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