Forming quality of liquid metal extrusion process has been difficult to ensure. We resolved this difficult problem by proposing modeling with artificial neural network (ANN) and optimization of process parameters with genetic algorithm (GA) . Fig.1 shows the neural network model that can make the five process parameters selected compatible: (1) pouring temperature (T1), (2) die temperature ( T2), (3) pressing velocity (v), (4) delaying period before applying p ressure (t), (5) deforming force (F). Then we optimized the five parameters with GA and obtained T1=716℃, T2=250℃, t=30 s, v≈2.6 ×10-3 m／s, Fmin=86.6 MPa for a liquid AlCuSiMgalloy extrusion . These predi cted optimal values agreed well with test results.%利用人工神经网络方法(ANN)建立了工艺系统模型，用遗传算法(GA)对过程参数进行优化， 实验结果与预测值吻合良好, 为预测和控制该工艺成形质量提供了行之有效的手段。
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