基于人工神经网络的亚共晶Al-7Si合金力学性能与显微组织定量关系分析

Quantitative Relationship Analysis Between Mechanical Properties and Microstructures of Al-7Si Aluminum Alloys by Artificial Neural Network

以亚共晶Al-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310~350 MPa,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18.56~33.04μm,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(<20μm)、变质共晶Si相(<12μm2)、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-7 Si 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.