以亚共晶A1-7Si合金为研究对象,基于Matlab神经网络工具箱开发了铝合金性能和组织关系预测程序,获得了高精度的材料性能与组织特征的关系预测模型.通过控制增压铸造过程中保压压力(85～300 kPa)和冷却速度(1～10 k/s)参数,获得具有不同力学性能和组织特征的铝合金.拉伸试样力学性能测试结果表明:抗拉强度为310～350MPa,延伸率为3％～12％.采用IPP 6.0软件统计组织特征参数结果表明:二次枝晶间距为18.56～33.04 μm,共晶Si相面积为6.37～13.37μm2,缺陷面积百分数为0％～0.363％,最大Fe相面积百分数为0％～0.06％.通过人工神经网络(ANN)预测模型,探究了单因素和双因素协同作用对合金力学性能的影响规律,建立了合金性能优化的组织控制路径.预测结果表明,该合金强度和塑性均与4种组织特征呈负相关,且缺陷和Fe相的存在对合金性能有较大的不利影响.因此,缩小枝晶间距(＜20 μm)、变质共晶Si相(＜12 μm2)、控制孔洞缺陷(＜0.35％)、严格控制富Fe相的尺寸和形态,是制备高性能铝合金的关键.“,”An artificial neural network model with high accuracy and good generation ability was developed to predict and optimize the mechanical properties of Al-7Si alloys.The results show that Al-7Si alloys with tensile strength of 310～350 MPa,elongation of 3％～12％,and different microstructures are obtained by controlling the holding pressure (85～300 kPa) and cooling rate (1～10 k/s) of the casting process.The quantitative correlation relationships of the mechanical properties with microstructures of the secondary dendrite arm spacing (18.56～33.04 μm),area of eutectic Si phase (6.37～13.37 μm2),area fraction of porosity defects (0％～0.363％),and area fraction of maximum Fe-rich intermetallics (0％～0.06％) in the alloy were established.The individual and combined influences of these microstructure characteristics on the mechanical properties were simulated.Both tensile strength and elongation are inversely related to the above-mentioned structural characteristics,and the presence of defects and Fe-rich intermetallics have great adverse effects on the properties of the alloy.Therefore,narrowing the dendrite spacing (＜20 μm),modifying the eutectic Si phase (＜12 μm2),and controlling the porosity defects (＜0.35％) and the morphology of the Fe-rich intermetallics are keys to prepare high-performance aluminum alloys.