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首页> 外文期刊>Arabian Journal for Science and Engineering. Section A, Sciences >Investigating the Optimum Model Parameters for Casting Process of A356 Alloy: A Cross-validation Using Response Surface Method and Particle Swarm Optimization
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Investigating the Optimum Model Parameters for Casting Process of A356 Alloy: A Cross-validation Using Response Surface Method and Particle Swarm Optimization

机译：研究A356合金铸造过程的最佳模型参数：响应面法和粒子群优化的交叉验证

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摘要

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 crosssectional 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 R~2 (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.
机译：本研究旨在确定A356合金最大流动性的最佳浇铸参数。使用重力压铸方法。为此，进行中央复合设计（CCD）。选择试验设计的输入参数及其限制作为预热温度（100-400°C），铸造温度（680-760℃）和横尖厚度（1-10mm）。使用基于CCD的进料距离的仿真结果，获得高度相关的全二次回归方程，以最高的R〜2（0.99）获得，然后用作粒子群优化（PSO）过程的目标函数。当输入参数分别选择为400°C，760°C和10mm时，响应参数的最高值达到高达491.19 mm。灵敏度分析表明，流动性上最有效的参数是横截面厚度。使用PSO方法还验证了响应曲面方法（RSM）的优化结果。尽管已经发现较高的温度导致更好的流动性，但是在诸如能源成本和模具寿命的较高温度下工作可能存在一些缺点。为了确定最佳输入参数，可以为任何类型的铸造过程进行修改本研究中建议的RSM模型。此外，特别是对于复杂的部分，可以建议制造商关于预热和浇铸温度的操作条件。

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