The Fe-based amorphous alloy coatings with different porosities were deposited on Q235 steel substrates by means of atmospheric plasma spraying(APS).The as-sprayed coatings were remelted by the facility of a Nd:YAG la...The Fe-based amorphous alloy coatings with different porosities were deposited on Q235 steel substrates by means of atmospheric plasma spraying(APS).The as-sprayed coatings were remelted by the facility of a Nd:YAG laser to further enhance their compactness and bonding strength via orthogonal experiment design.The effects of laser remelting on the microstructure,phase compositions and mechanical properties of the as-sprayed coatings were investigated by optical microscopy,scanning electron microscope,X-ray diffraction and Vickers microhardness tester.The corrosion performance of the coatings was evaluated by both potential dynamic measurements(PDM)and electrochemical impedance spectroscopy(EIS)in a 10%NaOH solution.The results indicate that laser power of 700 W,scanning velocity of 4 mm/s,beam size of 3 mm and porosity of 1.19%are the optimized remelting process parameters.The laser-remelted coatings exhibite more homogenous structure as strong metallurgical bonding to substrates.The amorphous phases in the as-sprayed coatings crystallize toα-Fe,Fe2Si,Fe3.5B,and Fe2W phases for the high temperature and rapid solidification in the remelting process.The microhardness values of as-sprayed are in the range of 700-800 HV0.1,while the microhardness values of the remelted coatings are enhanced slightly to 750-850 HV0.1.Both PDM and EIS analysis results show that the remelted coatings exhibite relatively excellent corrosion resistance compared with the stainless steel 1Cr18Ni9Ti,however the corrosion resistance of the remelted coatings is inferior to the as-sprayed amorphous coatings.展开更多
目的基于BP神经网络具有自学习、自训练和输出预测的功能,将其应用于热喷涂过程中的参数优化问题。方法依托高效能超音速等离子喷涂系统实验平台,以Fe基合金粉末为喷涂材料,将等离子喷涂中的主气流量、电功率和喷涂距离作为模型输入,涂...目的基于BP神经网络具有自学习、自训练和输出预测的功能,将其应用于热喷涂过程中的参数优化问题。方法依托高效能超音速等离子喷涂系统实验平台,以Fe基合金粉末为喷涂材料,将等离子喷涂中的主气流量、电功率和喷涂距离作为模型输入,涂层沉积速率和硬度作为模型输出,不断调整隐含层节点个数,最终建立3-7-2网络结构的BP神经网络以优化工艺参数。利用优化出的工艺参数制备Fe基合金涂层,测试其性能,并计算误差。结果神经网络优化出的最优喷涂工艺参数为:主气流量96L/min,电功率56 k W,喷涂距离95 mm。采用该工艺参数制备涂层,涂层增厚实测平均值为360μm,硬度为672HV0.3,而模型的预测值分别为332μm和611HV0.3,与预测值的相对误差分别为7.8%和9.1%。结论 BP神经网络对等离子喷涂参数优化问题的拟合精度比较高,误差在可以接受的范围之内。将BP神经网络运用于热喷涂工艺参数的优化具有科学性和可操作性。展开更多
基金National Natural Science Foundation of China(50805104)
文摘The Fe-based amorphous alloy coatings with different porosities were deposited on Q235 steel substrates by means of atmospheric plasma spraying(APS).The as-sprayed coatings were remelted by the facility of a Nd:YAG laser to further enhance their compactness and bonding strength via orthogonal experiment design.The effects of laser remelting on the microstructure,phase compositions and mechanical properties of the as-sprayed coatings were investigated by optical microscopy,scanning electron microscope,X-ray diffraction and Vickers microhardness tester.The corrosion performance of the coatings was evaluated by both potential dynamic measurements(PDM)and electrochemical impedance spectroscopy(EIS)in a 10%NaOH solution.The results indicate that laser power of 700 W,scanning velocity of 4 mm/s,beam size of 3 mm and porosity of 1.19%are the optimized remelting process parameters.The laser-remelted coatings exhibite more homogenous structure as strong metallurgical bonding to substrates.The amorphous phases in the as-sprayed coatings crystallize toα-Fe,Fe2Si,Fe3.5B,and Fe2W phases for the high temperature and rapid solidification in the remelting process.The microhardness values of as-sprayed are in the range of 700-800 HV0.1,while the microhardness values of the remelted coatings are enhanced slightly to 750-850 HV0.1.Both PDM and EIS analysis results show that the remelted coatings exhibite relatively excellent corrosion resistance compared with the stainless steel 1Cr18Ni9Ti,however the corrosion resistance of the remelted coatings is inferior to the as-sprayed amorphous coatings.
文摘目的基于BP神经网络具有自学习、自训练和输出预测的功能,将其应用于热喷涂过程中的参数优化问题。方法依托高效能超音速等离子喷涂系统实验平台,以Fe基合金粉末为喷涂材料,将等离子喷涂中的主气流量、电功率和喷涂距离作为模型输入,涂层沉积速率和硬度作为模型输出,不断调整隐含层节点个数,最终建立3-7-2网络结构的BP神经网络以优化工艺参数。利用优化出的工艺参数制备Fe基合金涂层,测试其性能,并计算误差。结果神经网络优化出的最优喷涂工艺参数为:主气流量96L/min,电功率56 k W,喷涂距离95 mm。采用该工艺参数制备涂层,涂层增厚实测平均值为360μm,硬度为672HV0.3,而模型的预测值分别为332μm和611HV0.3,与预测值的相对误差分别为7.8%和9.1%。结论 BP神经网络对等离子喷涂参数优化问题的拟合精度比较高,误差在可以接受的范围之内。将BP神经网络运用于热喷涂工艺参数的优化具有科学性和可操作性。
