Squeeze casting process parameters are the key to squeeze casting production and to obtain excellent performance casts. To realize intelligent optimization design of process parameters under various requirements, this work presents a new intelligent optimization design framework for squeeze casting process parameters based on process data and integrating a two-stage intelligent integrated optimization. To adapt to diverse optimization applications of different process and target parameters, the backpropagation (BP) neural network and existing process data are utilized to intelligently establish the incompletely determined correlations between process parameters and squeeze cast quality or properties. An improved sparrow search algorithm (LCSSA) is developed and integrated to optimize both the aforementioned model structure and intelligently obtain the optimal solution, that is, the two stages of optimization. For the purpose of assessing the effect of each performance on the combination of process parameters, the information entropy weight approach is used. Various application cases and experiments have been conducted to evaluate the effectiveness of the suggested intelligent optimization design framework. It is indicated that the proposed method is practicable and can intelligently achieve ideal process parameters with high accuracy even based on small-scale data samples. The solving efficiency and optimization accuracy of LCSSA are superior than those of other intelligent optimization algorithms like the genetic algorithm (GA). The proposed framework outperforms the mixture of the BP neural network and other traditional optimization algorithms.

中文翻译：

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基于神经网络和改进麻雀搜索算法的挤压铸造工艺参数智能优化设计

挤压铸造工艺参数是挤压铸造生产和获得性能优良铸件的关键。为了实现各种需求下工艺参数的智能优化设计，本文提出了一种基于工艺数据、集成两阶段智能集成优化的挤压铸造工艺参数智能优化设计框架。为了适应不同工艺和目标参数的多样化优化应用，利用反向传播（BP）神经网络和现有工艺数据智能地建立工艺参数和挤压铸造质量或性能之间不完全确定的相关性。开发并集成了改进的麻雀搜索算法（LCSSA），以优化上述模型结构并智能地获得最优解，即优化的两个阶段。为了评估每种性能对工艺参数组合的影响，采用信息熵权法。已经进行了各种应用案例和实验来评估所建议的智能优化设计框架的有效性。结果表明，该方法切实可行，即使基于小规模数据样本，也能高精度、智能地获得理想工艺参数。 LCSSA的求解效率和优化精度优于遗传算法（GA）等其他智能优化算法。所提出的框架优于 BP 神经网络和其他传统优化算法的混合。