| 黄海峰， 王东方， 康正阳.A356铝合金飞轮壳挤压铸造工艺参数优化[J].轻工机械,2022,40(6):22-26 |
| A356铝合金飞轮壳挤压铸造工艺参数优化 |
| Optimization of Squeeze Casting Process Parameters for A356 Aluminum Alloy Flywheel Housing |
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| DOI：10.3969/j.issn.1005 2895.2022.06.004 |
| 中文关键词: 铝合金飞轮壳 挤压铸造 遗传算法 BP神经网络 粒子群算法 |
| 英文关键词:aluminum alloy flywheel housing squeeze casting genetic algorithm Back Propagation Neural Network PSO(Particle Swarm Optimization) |
| 基金项目:江苏省高等学校自然科学研究面上项目（19KJB460005）；江苏省博士后创新基金面上项目（2020Z410）；江苏省产学研合作项目（BY2019006）。 |
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| 中文摘要: |
| 为探究最优的A356铝合金飞轮壳挤压铸造工艺参数组合，课题组研究了运用铸造数值模拟仿真、建立遗传算法优化后的BP神经网络及粒子群算法，以最小缩松缩孔缺陷体积作为优化目标，对浇铸温度、模具预热温度、比压和保压时间4个参数进行优化。结果表明：优化的工艺参数组合为浇注温度676.2 ℃，模具预热温度280 ℃，比压66.5 MPa，保压时间40 s 。优化后参数组合能够有效提高铸件成形质量及力学性能。 |
| 英文摘要: |
| In order to explore the optimal combination of process parameters for the extrusion casting of A356 aluminum alloy flywheel housing, the application of casting numerical simulation and BP neural network optimized by genetic algorithm and particle swarm algorithm.The minimum shrinkage porosity defect volume value was taked as the optimization goal to select and optimize the four parameters of casting temperature, mold preheating temperature, specific pressure and pressure holding time. The results show that the optimized combination of process parameters is the pouring temperature of 676.2 ℃, the mold preheating temperature of 280 ℃, the specific pressure of 66.5 MPa, and the pressure holding time of 40 s. The above optimized parameters combination can effectively improve the casting quality and mechanical properties. |
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