基于代理模型的锻造模具结构智能优化研究
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摘要
锻造是金属塑性加工中常用的加工方法,由于材料经过锻造加工之后具有各项优良的机械性能,所以广泛应用于各种机械产品的加工。锻造模具是锻造生产中的主要装备,其设计和制造的质量以及使用寿命决定了锻件的质量和成本。对锻件质量的控制,主要是要对锻造模具进行控制。
影响锻造工艺及锻件质量的因素可归纳为模具结构形状设计、模具材料、模具加工、锻件复杂程度、设备性能等因素。由于在金属塑性变形过程中材料的流动主要受模具形状的影响及控制,因此,合理选择与设计模具结构的形状参数就显得尤为重要。
随着数值模拟仿真技术日益成熟,基于模拟的设计方法在塑性成形工程中得到了广泛应用。这种设计方法是应用有限元技术对金属塑性成形过程中的应力应变进行计算,在后处理结果中直观地分析成形过程中金属的流动规律以及设计变量对成形过程的影响,判断是否会产生成形缺陷,预测成形载荷,然后对工艺参数和模具形状的进行修改。
为了提高锻造模具的设计效率、降低制造成本和提高产品质量,有必要对锻造工艺及其模具结构中影响锻件质量的各项工艺参数进行优化。目前,基于有限元分析的优化设计方法在锻造成形工艺及其模具设计中的已成为一种趋势。作为基于有限元分析的优化方法之一,基于目标函数值的拟合优化方法因其通用性好而最具推广价值。基于目标函数值的拟合优化方法,其特点是优化与有限元程序分离,通用性强。可直接利用现有的商用有限元分析软件,充分发挥其强大的有限元计算功能。基于目标函数值的拟合优化方法,其实质是代理模型方法,即用拟合的方法建立近似模型,通过近似模型逼近目标函数和设计变量之间的函数关系,然后求解这个近似模型的极值点来逼近真实的极值点。
基于目标函数值的拟合优化方法中,关键是要通过一定的拟合方法,建立起能够正确反映设计变量与目标函数之间关系的近似模型。
为了能够正确地反映设计变量各个参数的重要性,必须采用合理的试验设计方法获得所需的样本点。得到足够的样本点后,通过一定的机理模型,采用数值模拟程序进行求解,获得所关心的目标函数值。然后选择合适的近似模型构建方法进行拟合。最后,对得到的近似模型进行优化分析(低维的采用常规线性规划或非线性规划,高维的采用智能优化算法)。
由于金属塑性成形问题的多因素高维非线性无法用常规优化迭代方法寻优,而智能优化方法可以不用求导数,且全局探索能力强,非常适用于塑性成形问题的优化。另外,充分考虑到Kriging模型适于对高维非线性问题进行插值拟合的特点。本文将Kriging模型与遗传算法(Genetic Algorithms,GA)相耦合,提出Kriging-GA优化策略,用于锻造模具结构参数的优化设计。Kriging-GA优化策略由三部分组成:近似模型的构建;多目标问题的变换;遗传算法寻优。Kriging模型的构建与遗传算法寻优通过在Matlab下编程进行耦合。
与遗传算法比较而言,粒子群算法容易实现,并且由于其不需要遗传交叉、变异等操作,使之需要调整的参数较少。另外,粒子群算法具有收敛速度快的优点。本文将Kriging模型与粒子群优化算法(Particle Swarm Optimization,PSO)相耦合,首次提出了Kriging-PSO优化策略,在Matlab下编程实现。
将Kriging-GA优化策略用于汽车法兰盘锻模和汽车曲轴锻模的优化中,与多项式响应面方法进行了对比。研究结果表明,Kriging-GA优化法较多项式响应面方法的预测精度高,但收敛慢。在此基础上,将Kriging-PSO优化策略应用于该汽车曲轴锻模的优化问题作为对比。结果表明,与Kriging-GA法所得优化结果基本一致,但收敛速度提高数十倍。最后,将Kriging-PSO优化策略应用于射孔弹冷挤压的预挤压成形和终成形组合凹模的优化设计中,验证了Kriging-PSO优化策略的有效性。
Forging is the commonly used processing method in metal plastic processing. Due to the material after forging have excellent mechanical properties, so this method widely applied in all kinds of mechanical products processing. As forging die is the main equipment in forging production, the quality of its design and manufacturing and service life determines the quality and cost of forgings. The quality of forgings mainly depends on the quality of forging die.
The influence factors of forging process and forgings quality can be summarized as the mold structure and shape design, mould material, mould processing, forgings complexity, equipment performance, and other factors. The material flow during metal plastic deformation process mainly is influenced and controlled by the mold shape, therefore, reasonable selection and design of the mold structure shape parameters are particularly important.
With the numerical simulation technology has become more mature, simulation-based design method in plastic forming technology has been widely used. This design approach is to calculate stress and strain in the metal forming process using finite element technology, and to determine whether the defects would have formed and to predict forming load, and then modify the process parameters and mold shapes through an intuitive analysis of metal forming process flow pattern and design variables on the impact of forming process in Post-processing results.
In order to improve the forging die design efficiency and reduce manufacturing costs and improve product quality, it is necessary to optimize the forging process parameters in forging process and die structure that affect the quality of the forgings. At present, the optimum design method based on finite element analysis in forging process and its die design has become a trend.
As one of the optimization method based on finite element analysis, the fitting optimization methods based on the objective function value is the method to be popularized currently because of its good general characteristics. The fitting optimization methods based on the objective function value, which is characterized by separation of optimization and the finite element program and versatility, can be directly use the existing commercial FEM software, give full play to its powerful finite element functions. The fitting optimization methods based on the objective function value is essentially agent model method. This method is to establish approximate model by fitting and to approximate the functional relationship between objective function and variables and then solving this approximate model to approximate the true extreme point.
The key in the fitting optimization methods based on the objective function value is to establish the approximate model which can correctly reflect the relationship between the design variables and target function.
In order to correctly reflect the importance of each parameter design variables, the reasonable experimental design method must be adopt to obtain the necessary sample points. After get enough sample point, the objective function value can be obtained using numerical simulation program for solving through a certain mechanism model. Then select the appropriate method to build approximate models. Finally, the approximate models were optimized analysis.
Conventional optimization iterative method can not be used in the metal forming optimization problems because multi-factor、high-dimensional and non-linear. Intelligent optimization method is ideal for optimization of metal forming problems because it can not calculate derivative and the good global exploration ability. In addition, the Kriging model is suitable for high-dimensional nonlinear interpolation problems. This paper, the Kriging model coupled with genetic algorithms is proposed Kriging-GA optimization strategies for forging die structure parameters of optimal design. In this paper, Kriging-GA optimization strategies for optimal design of forging die structure parameters are proposed by the Kriging model and genetic algorithms coupled. Kriging-GA optimization strategy consists of three parts: building approximate model; the transformation of the multi-objective problem; genetic algorithm optimization. Kriging model Construction and genetic algorithm optimization program carried out by coupling using Matlab.
Comparison with genetic algorithms, particle swarm algorithm has the advantages of easy to realize and fast convergence and few parameters need to be adjusted because it does not need genetic crossover and mutation operation. This article first propose Kriging-PSO optimization strategy coupled with kriging model and the particle swarm algorithms and achieve under Matlab programming.
The Kriging-GA strategy and Polynomial response surface method were compared with applied in optimization for automotive flange forging die and crankshaft forging die. The results show that, Kriging-GA method has more accuracy but slow convergence than the polynomial response surface methods. On this basis, the Kriging-PSO optimization strategies applied to the crankshaft forging die as a contrast to the optimization problem. The results showed that results obtained with the Kriging-GA and with the Kriging-PSO had basically the same, but the Kriging-PSO convergence speed fast several dozen times. Finally, the Kriging-PSO optimization strategy used in cold extrusion of pre-perforated shells and end-forming combination of extrusion die of the optimal design to verify the Kriging-PSO optimization strategy is effective.
影响锻造工艺及锻件质量的因素可归纳为模具结构形状设计、模具材料、模具加工、锻件复杂程度、设备性能等因素。由于在金属塑性变形过程中材料的流动主要受模具形状的影响及控制,因此,合理选择与设计模具结构的形状参数就显得尤为重要。
随着数值模拟仿真技术日益成熟,基于模拟的设计方法在塑性成形工程中得到了广泛应用。这种设计方法是应用有限元技术对金属塑性成形过程中的应力应变进行计算,在后处理结果中直观地分析成形过程中金属的流动规律以及设计变量对成形过程的影响,判断是否会产生成形缺陷,预测成形载荷,然后对工艺参数和模具形状的进行修改。
为了提高锻造模具的设计效率、降低制造成本和提高产品质量,有必要对锻造工艺及其模具结构中影响锻件质量的各项工艺参数进行优化。目前,基于有限元分析的优化设计方法在锻造成形工艺及其模具设计中的已成为一种趋势。作为基于有限元分析的优化方法之一,基于目标函数值的拟合优化方法因其通用性好而最具推广价值。基于目标函数值的拟合优化方法,其特点是优化与有限元程序分离,通用性强。可直接利用现有的商用有限元分析软件,充分发挥其强大的有限元计算功能。基于目标函数值的拟合优化方法,其实质是代理模型方法,即用拟合的方法建立近似模型,通过近似模型逼近目标函数和设计变量之间的函数关系,然后求解这个近似模型的极值点来逼近真实的极值点。
基于目标函数值的拟合优化方法中,关键是要通过一定的拟合方法,建立起能够正确反映设计变量与目标函数之间关系的近似模型。
为了能够正确地反映设计变量各个参数的重要性,必须采用合理的试验设计方法获得所需的样本点。得到足够的样本点后,通过一定的机理模型,采用数值模拟程序进行求解,获得所关心的目标函数值。然后选择合适的近似模型构建方法进行拟合。最后,对得到的近似模型进行优化分析(低维的采用常规线性规划或非线性规划,高维的采用智能优化算法)。
由于金属塑性成形问题的多因素高维非线性无法用常规优化迭代方法寻优,而智能优化方法可以不用求导数,且全局探索能力强,非常适用于塑性成形问题的优化。另外,充分考虑到Kriging模型适于对高维非线性问题进行插值拟合的特点。本文将Kriging模型与遗传算法(Genetic Algorithms,GA)相耦合,提出Kriging-GA优化策略,用于锻造模具结构参数的优化设计。Kriging-GA优化策略由三部分组成:近似模型的构建;多目标问题的变换;遗传算法寻优。Kriging模型的构建与遗传算法寻优通过在Matlab下编程进行耦合。
与遗传算法比较而言,粒子群算法容易实现,并且由于其不需要遗传交叉、变异等操作,使之需要调整的参数较少。另外,粒子群算法具有收敛速度快的优点。本文将Kriging模型与粒子群优化算法(Particle Swarm Optimization,PSO)相耦合,首次提出了Kriging-PSO优化策略,在Matlab下编程实现。
将Kriging-GA优化策略用于汽车法兰盘锻模和汽车曲轴锻模的优化中,与多项式响应面方法进行了对比。研究结果表明,Kriging-GA优化法较多项式响应面方法的预测精度高,但收敛慢。在此基础上,将Kriging-PSO优化策略应用于该汽车曲轴锻模的优化问题作为对比。结果表明,与Kriging-GA法所得优化结果基本一致,但收敛速度提高数十倍。最后,将Kriging-PSO优化策略应用于射孔弹冷挤压的预挤压成形和终成形组合凹模的优化设计中,验证了Kriging-PSO优化策略的有效性。
Forging is the commonly used processing method in metal plastic processing. Due to the material after forging have excellent mechanical properties, so this method widely applied in all kinds of mechanical products processing. As forging die is the main equipment in forging production, the quality of its design and manufacturing and service life determines the quality and cost of forgings. The quality of forgings mainly depends on the quality of forging die.
The influence factors of forging process and forgings quality can be summarized as the mold structure and shape design, mould material, mould processing, forgings complexity, equipment performance, and other factors. The material flow during metal plastic deformation process mainly is influenced and controlled by the mold shape, therefore, reasonable selection and design of the mold structure shape parameters are particularly important.
With the numerical simulation technology has become more mature, simulation-based design method in plastic forming technology has been widely used. This design approach is to calculate stress and strain in the metal forming process using finite element technology, and to determine whether the defects would have formed and to predict forming load, and then modify the process parameters and mold shapes through an intuitive analysis of metal forming process flow pattern and design variables on the impact of forming process in Post-processing results.
In order to improve the forging die design efficiency and reduce manufacturing costs and improve product quality, it is necessary to optimize the forging process parameters in forging process and die structure that affect the quality of the forgings. At present, the optimum design method based on finite element analysis in forging process and its die design has become a trend.
As one of the optimization method based on finite element analysis, the fitting optimization methods based on the objective function value is the method to be popularized currently because of its good general characteristics. The fitting optimization methods based on the objective function value, which is characterized by separation of optimization and the finite element program and versatility, can be directly use the existing commercial FEM software, give full play to its powerful finite element functions. The fitting optimization methods based on the objective function value is essentially agent model method. This method is to establish approximate model by fitting and to approximate the functional relationship between objective function and variables and then solving this approximate model to approximate the true extreme point.
The key in the fitting optimization methods based on the objective function value is to establish the approximate model which can correctly reflect the relationship between the design variables and target function.
In order to correctly reflect the importance of each parameter design variables, the reasonable experimental design method must be adopt to obtain the necessary sample points. After get enough sample point, the objective function value can be obtained using numerical simulation program for solving through a certain mechanism model. Then select the appropriate method to build approximate models. Finally, the approximate models were optimized analysis.
Conventional optimization iterative method can not be used in the metal forming optimization problems because multi-factor、high-dimensional and non-linear. Intelligent optimization method is ideal for optimization of metal forming problems because it can not calculate derivative and the good global exploration ability. In addition, the Kriging model is suitable for high-dimensional nonlinear interpolation problems. This paper, the Kriging model coupled with genetic algorithms is proposed Kriging-GA optimization strategies for forging die structure parameters of optimal design. In this paper, Kriging-GA optimization strategies for optimal design of forging die structure parameters are proposed by the Kriging model and genetic algorithms coupled. Kriging-GA optimization strategy consists of three parts: building approximate model; the transformation of the multi-objective problem; genetic algorithm optimization. Kriging model Construction and genetic algorithm optimization program carried out by coupling using Matlab.
Comparison with genetic algorithms, particle swarm algorithm has the advantages of easy to realize and fast convergence and few parameters need to be adjusted because it does not need genetic crossover and mutation operation. This article first propose Kriging-PSO optimization strategy coupled with kriging model and the particle swarm algorithms and achieve under Matlab programming.
The Kriging-GA strategy and Polynomial response surface method were compared with applied in optimization for automotive flange forging die and crankshaft forging die. The results show that, Kriging-GA method has more accuracy but slow convergence than the polynomial response surface methods. On this basis, the Kriging-PSO optimization strategies applied to the crankshaft forging die as a contrast to the optimization problem. The results showed that results obtained with the Kriging-GA and with the Kriging-PSO had basically the same, but the Kriging-PSO convergence speed fast several dozen times. Finally, the Kriging-PSO optimization strategy used in cold extrusion of pre-perforated shells and end-forming combination of extrusion die of the optimal design to verify the Kriging-PSO optimization strategy is effective.
引文
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