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首页> 外文期刊>Journal of Materials Engineering and Performance >Optimization of LPDC Process Parameters Using the Combination of Artificial Neural Network and Genetic Algorithm Method
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Optimization of LPDC Process Parameters Using the Combination of Artificial Neural Network and Genetic Algorithm Method

机译：结合人工神经网络和遗传算法优化LPDC工艺参数

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In this article, the low-pressure die-cast (LPDC) process parameters of aluminum alloy thin-walled component with permanent mold are optimized using a combining artificial neural network and genetic algorithm (ANN/GA) method. In this method, an ANN model combining learning vector quantization (LVQ) and back-propagation (BP) algorithm is proposed to map the complex relationship between process conditions and quality indexes of LPDC. The genetic algorithm is employed to optimize the process parameters with the fitness function based on the trained ANN model. Then, by applying the optimized parameters, a thin-walled component with 300 mm in length, 100 mm in width, and 1.5 mm in thickness is successfully prepared and no obvious defects such as shrinkage, gas porosity, distortion, and crack were found in the component. The results indicate that the combining ANN/GA method is an effective tool for the process optimization of LPDC, and they also provide valuable reference on choosing the right process parameters for LPDC thin-walled aluminum alloy casting.
机译：本文采用人工神经网络和遗传算法（ANN / GA）相结合的方法，对具有永久铸型的铝合金薄壁构件的低压压铸（LPDC）工艺参数进行了优化。在该方法中，提出了一种将学习向量量化（LVQ）和反向传播（BP）算法相结合的ANN模型，以映射工艺条件和LPDC质量指标之间的复杂关系。基于训练后的人工神经网络模型，采用遗传算法通过适应度函数对工艺参数进行优化。然后，通过应用优化的参数，成功地制备了长度为300 mm，宽度为100 mm，厚度为1.5 mm的薄壁部件，并且没有发现明显的缺陷，如收缩，气孔，变形和裂纹。组件。结果表明，ANN / GA组合方法是LPDC工艺优化的有效工具，为选择LPDC薄壁铝合金铸件的正确工艺参数提供了有价值的参考。

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