基于智能算法和数值模拟协同的凸轮轴锻造优化

Optimization of camshaft forging based on intelligent algorithm and numerical simulation

为了解决凸轮轴热锻过程中的材料浪费和成型载荷较大的问题,基于遗传算法、克里金预测和数值模拟的动态协同技术,提出了将填充、折叠作为约束的热模锻坯料优化策略。在优化过程中,克里金模型用于构建局部区域的梯度信息,并用数值模拟对克里金模型进行动态矫正,遗传算法控制设计变量的进化方向。用凸轮轴锻件作为优化案例,并对凸轮轴锻件的形状进行分析,设计了一种坯料形状并选用其3个尺寸参数作为优化参数。优化结果表明:该方法能够优化3D成型问题,可显著减少下料体积、节省大约27.81%材料。优化后的坯料能够满足充填完整约束、无折叠约束,并且优化后的成型载荷降低了大约71.43%左右,优化后的设计变量取值为D_(1)=40.6,D_(2)=60,D_(3)=16.6。

In order to solve the problems of material waste and large forming load in the process of camshaft hot forging,a hot die forging blank optimization strategy with filling and folding as constraints is proposed based on the dynamic collaborative technology of genetic algorithm,Kriging prediction and numerical simulation.In the optimization process,the Kriging model is used to construct the gradient information of the local region,and the Kriging model is dynamically corrected by numerical simulation,and the genetic algorithm controls the evolution direction of the design variables.In this paper,camshaft forging is taken as an optimization case,the shape of camshaft forging is analyzed,the blank shape is designed,and its three-dimensional parameters is selected as optimization parameters.The optimization results show that the 3 D forming problem can be optimized by this method,which significantly reduce the blanking volume and save about 27.81%of materials.The optimized blank can meet the filling integrity constraint and no folding constraint,and the optimized forming load is significantly reduced by about 71.43%.The optimized design variables are D_(1)=40.6,D_(2)=60 and D_(3)=16.6.