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应用Ｇｌｅｅｂｌｅ－１５００热模拟试验机研究了１Ｃｒ１８Ｎｉ９Ｔｉ的热墩成形过程，得出了流动应力随变形条件的变化规律，测量了空冷后试件的硬度．首次采用人工神经网络对锻造性能进行预报，采用不同的改进ＢＰ算法加速了网络的收敛，得到了较好的网络信息，对１Ｃｒ１８Ｎｉ９Ｔｉ的锻造性能进行了较准确预报．
The hot pier forming process of 1Cr18Ni9Ti was studied with Gleeble-1500 thermal simulation tester. The variation of flow stress with deformation conditions was obtained. The hardness of the specimen after air cooling was measured. The artificial neural network was used to predict the forging performance for the first time. Different improved BP algorithms were used to accelerate the convergence of the network. Better network information was obtained and the forging performance of 1Cr18Ni9Ti was predicted more accurately.