Changing of temperature distribution during warm shear spinning of cone workpiece of titanium alloy greatly affects the process and the product's precision.
成形过程中的温度场变化对钛合金锥形件温热剪旋成功与否，以及产品的精度影响很大。
BP neutral networks model has been established in order to predict process parameter of shear spinning based on many data accumulated during manufacturing.
以生产数据为学习样本，建立BP神经网络模型，实现对剪切旋压工艺参数的预测。
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