This study aimed to determine the optimal casting parameters for the maximum fluidity of A356 alloy. Gravity die cast method was used. For this purpose, central composite design (CCD) was performed. The input parameters and their limits for the trial design were selected as pre-heating temperature (100–400 °C), casting temperature (680–760 °C), and cross-sectional thickness (1–10 mm). Using the CCD-based simulation results of the feed distance, a highly correlated full-quadratic regression equation was obtained with the highest R² (0.99), which then was used as the objective function for the particle swarm optimization (PSO) process. The highest value of the response parameter, flow distance, reached up to 491.19 mm when the input parameters were selected as 400 °C, 760 °C and 10 mm, respectively. The sensitivity analysis has shown that the most effective parameter on the fluidity is the cross-sectional thickness. The response surface method (RSM)-based optimization results have been also validated using the PSO method. Although the higher temperatures have been found to result in better fluidity, there may be some drawbacks to working at higher temperatures such as energy cost and mould life. To determine the optimum input parameters, the RSM model suggested in this study can be modified for any type of casting process. Moreover, especially for a complex-shaped part, the manufacturer can be advised regarding operating conditions such as pre-heating and casting temperatures.

这项研究旨在确定A356合金最大流动性的最佳铸造参数。使用重力压铸法。为此目的，进行了中央复合设计（CCD）。选择用于试验设计的输入参数及其限值，例如预热温度（100–400°C），铸造温度（680–760°C）和横截面厚度（1–10 mm）。使用基于CCD的进给距离仿真结果，获得了具有最高R ²的高度相关的全二次回归方程（0.99），然后将其用作粒子群优化（PSO）过程的目标函数。当输入参数分别选择为400°C，760°C和10 mm时，响应参数的最大值（流动距离）达到491.19 mm。敏感性分析表明，对流动性最有效的参数是横截面厚度。基于响应面法（RSM）的优化结果也已使用PSO方法进行了验证。尽管已经发现较高的温度会导致更好的流动性，但是在较高的温度下工作可能会存在一些缺点，例如能源成本和模具寿命。为了确定最佳输入参数，可以针对任何类型的铸造工艺修改本研究中建议的RSM模型。而且，特别是对于复杂形状的零件，