A method utilizing variable depth increments during incremental forming was proposed and then optimized based on numerical simulation and intelligent algorithm.Initially,a finite element method(FEM) model was set up a...A method utilizing variable depth increments during incremental forming was proposed and then optimized based on numerical simulation and intelligent algorithm.Initially,a finite element method(FEM) model was set up and then experimentally verified.And the relation between depth increment and the minimum thickness tmin as well as its location was analyzed through the FEM model.Afterwards,the variation of depth increments was defined.The designed part was divided into three areas according to the main deformation mechanism,with Di(i=1,2) representing the two dividing locations.And three different values of depth increment,Δzi(i=1,2,3) were utilized for the three areas,respectively.Additionally,an orthogonal test was established to research the relation between the five process parameters(D and Δz) and tmin as well as its location.The result shows that Δz2 has the most significant influence on the thickness distribution for the corresponding area is the largest one.Finally,a single evaluating indicator,taking into account of both tmin and its location,was formatted with a linear weighted model.And the process parameters were optimized through a genetic algorithm integrated with an artificial neural network based on the evaluating index.The result shows that the proposed algorithm is satisfactory for the optimization of variable depth increment.展开更多
针对传统的最大功率点追踪(Maximum Power Point Tracking,MPPT)算法陷入局部极值不能找到最大功率点(Maximum Power Point,MPP)以及传统的蝴蝶优化算法(Butterfly Optimization Algorithm,BOA)存在收敛速度慢和搜索震荡较大等问题,提...针对传统的最大功率点追踪(Maximum Power Point Tracking,MPPT)算法陷入局部极值不能找到最大功率点(Maximum Power Point,MPP)以及传统的蝴蝶优化算法(Butterfly Optimization Algorithm,BOA)存在收敛速度慢和搜索震荡较大等问题,提出一种改进的蝴蝶优化算法(Improved Butterfly Optimization Algorithm,IBOA)结合电导增量法(Conductance Increment Method,INC)的复合MPPT追踪方法。在IBOA中,引入自适应动态转换概率来平衡算法的全局与局部搜索,然后在全局搜索阶段引入Levy飞行策略,使蝴蝶个体广泛分布于搜索空间中,提高全局寻优能力;同时在局部搜索中设置新的寻优对象,并通过贪婪算法进行筛选保留,提高局部搜索的能力。当系统位于MPP附近时,利用INC局部搜索能力强的优点快速、准确地收敛到MPP并且稳定功率的输出。仿真结果表明,在静态和动态阴影下与BOA、PSO算法进行对比,所提算法具有更快的追踪速度、更高的追踪效率和更强的鲁棒性。展开更多
文摘A method utilizing variable depth increments during incremental forming was proposed and then optimized based on numerical simulation and intelligent algorithm.Initially,a finite element method(FEM) model was set up and then experimentally verified.And the relation between depth increment and the minimum thickness tmin as well as its location was analyzed through the FEM model.Afterwards,the variation of depth increments was defined.The designed part was divided into three areas according to the main deformation mechanism,with Di(i=1,2) representing the two dividing locations.And three different values of depth increment,Δzi(i=1,2,3) were utilized for the three areas,respectively.Additionally,an orthogonal test was established to research the relation between the five process parameters(D and Δz) and tmin as well as its location.The result shows that Δz2 has the most significant influence on the thickness distribution for the corresponding area is the largest one.Finally,a single evaluating indicator,taking into account of both tmin and its location,was formatted with a linear weighted model.And the process parameters were optimized through a genetic algorithm integrated with an artificial neural network based on the evaluating index.The result shows that the proposed algorithm is satisfactory for the optimization of variable depth increment.
文摘针对传统的最大功率点追踪(Maximum Power Point Tracking,MPPT)算法陷入局部极值不能找到最大功率点(Maximum Power Point,MPP)以及传统的蝴蝶优化算法(Butterfly Optimization Algorithm,BOA)存在收敛速度慢和搜索震荡较大等问题,提出一种改进的蝴蝶优化算法(Improved Butterfly Optimization Algorithm,IBOA)结合电导增量法(Conductance Increment Method,INC)的复合MPPT追踪方法。在IBOA中,引入自适应动态转换概率来平衡算法的全局与局部搜索,然后在全局搜索阶段引入Levy飞行策略,使蝴蝶个体广泛分布于搜索空间中,提高全局寻优能力;同时在局部搜索中设置新的寻优对象,并通过贪婪算法进行筛选保留,提高局部搜索的能力。当系统位于MPP附近时,利用INC局部搜索能力强的优点快速、准确地收敛到MPP并且稳定功率的输出。仿真结果表明,在静态和动态阴影下与BOA、PSO算法进行对比,所提算法具有更快的追踪速度、更高的追踪效率和更强的鲁棒性。
文摘为提高双点渐进成形(double-side incremental sheet forming,DSIF)制件的成形精度,以方锥盒制件作为试验制件,以刀具直径、层间距、成形角、板厚和成形深度等工艺参数为影响因素,以底部回弹值和侧壁鼓凸最小值作为优化目标设计正交试验,利用Abaqus数值仿真计算出试验结果数据,通过建立多输入和多输出的BP(back propagation)神经网络预测模型,结合带精英策略的非支配排序遗传算法(non-dominated sorting genetic algorithm,NAGA-Ⅱ)求解双点渐进成形工艺参数多目标优化问题,基于熵权逼近理想解排序法(technique for order preference by similarity to ideal solution,TOPSIS)从Pareto解集中决策出一组最优工艺参数组合以提高优化结果的精确度,通过优化和筛选得到的最佳工艺参数组合进行对应试验。结果表明,经实测得到制件的底部回弹值为0.693 mm,侧壁鼓凸值为0.934 mm,筛选出的目标值误差分别为6.31%和2.09%。由此可见,建立的多目标优化流程具有可行性,为双点渐进成形制件的回弹减少提供了有效的优化方案。
