基于动态遗传神经网络的方盒件成形多目标优化
Multi-objective Optimization of Square Box Forming Based on Dynamic Genetic Neural Network Model
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摘要: 针对板料成形优化中采用传统静态代理模型存在全局近似精度不高、超量选取样本点等问题，提出了多重近似精度收敛、逐步增添样本点的动态遗传神经网络（Genetic Algorithm Back Propagation Neural Network，GABP）建模方法。样本点增补策略根据动态模型的全局近似精度和局部近似精度分别按最大最小距离增补和局部最优解增补。将动态代理模型应用于NUMISHEET 93方形盒冲压成形优化问题，结合灰色关联理论将多目标问题转化为单目标问题并构造用于优化的迭代格式，实现了方盒件成形的多目标优化，有效地提高了方盒件成形质量和优化计算效率。Abstract: Dynamic genetic algorithm back propagation(GABP) neural network modeling method is proposed with multiple precision convergence criteria and gradual increase of sample points, aiming at the problem that traditional static surrogate model has low global approximation accuracy and over selected sample points in sheet metal forming optimization. According to the global approximation accuracy and local approximation accuracy of the dynamic model, the supplement sample point adds by the maximum minimum distance and the local optimal solution respectively. Dynamic model is applied to optimization of the NUMISHEET 93 square box forming. Combining grey relational theory to convert multi-objective problems into single-objective problems and constructing iterative schemes for optimization, multi-objective optimization of square box forming is achieved. Optimization results show that the presented method effectively improve the forming quality and the calculation efficiency of square box.
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