为了进一步提高Ti-6Al-4V的性能, 以满足其在工程中更广泛的运用, 研究了在Ti-6Al-4V激光NiAl-VC合金化的工艺。以改变激光功率、激光扫描速度和粉末质量含量比例进行了工艺实验, 采用BP神经网络(BP-NN)算法, 建立了合金化层性能与工艺参数之间的关系模型, 并通过验证实验表明预测效果良好, 具有可行性。采用BP-NN算法进行了模拟实验, 分析了不同工艺参数条件对合金化层深度、宽度、平均硬度、最高硬度的影响规律。本研究对Ti-6Al-4V激光NiAl-VC合金化的实践应用具有指导意义和参考价值。

In order to further improve the performance of Ti-6Al-4V, this paper makes it meet the more widely used in engineering, then trying to study the laser alloying process on Ti-6Al-4V surface using NiAl-VC powder. Experiments with the change of laser power and scanning speed and the mass ratio of powder content were preceded. The relation model between alloyed layer performances and process parameters was established by using BP neural network (BP-NN) algorithm. Experimental verification shows that, the prediction effect is nice and relatively feasible. The influence of different process parameters on the properties, such as depth, width, average hardness and maximum hardness of the alloyed layer, was analyzed by the simulation based on BP neural network algorithm. This research embodies guiding significance and reference value to the application of the laser alloying process on Ti-6Al-4V surface in the industrial manufacturing.