基于BP神经网络的等离子喷涂涂层摩擦磨损性能分析

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The KF301/WS2 complex lubricating wear-resisting coatings with different surface texturing structure were prepared by supersonic plasma spraying technologies and laser surface texturing technologies. The friction and wear proper- ties of the coatings were studied under the conditions of different temperatures, surface textures and content of lubricant. A nonlinear model of relationship of friction coefficient and wear rate with temperatures, surface textures and content of lubri- cant was set up by BP neural network technology. And the results obtained are in consistent with the test results. The analy- sis results indicate that when the temperature is between 300 - 600 ℃, wear mass loss and friction coefficient are increased along with the rising of temperature, but quite slow. Moreover when the temperature is between 600 - 750℃, wear mass loss and friction coefficient are increased quickly with the rising of temperature. At the same temperature and same content of WS2, the friction and wear properties of surfaces of different textures from high to low, in turn, is round pit, diamond, parallel and intermittence. At the same temperature and same texture surface, the wear properties of the coating containing 30% WS2 are a little better than those of the coating containing 20% WS2.采用超音速等离子喷涂技术、激光表面微造型技术制备不同表面微造型的KF301／WS2复合润滑耐磨涂层，研究涂层在不同温度、表面造型和润滑剂含量下的摩擦磨损性能。利用BP神经网络技术建立涂层摩擦因数和磨损量与温度、表面微造型、润滑剂含量的非线性关系模型，得到的结果和试验结果吻合得较好。分析结果表明：当温度在300～600℃时，磨损量和摩擦因数随着温度的升高而增大，但增长较缓慢，而当温度在600～750℃时，摩擦量和摩擦因数随着温度升高增长较快；在同一温度和同-WS2含量的情况下，不同微造型面的摩擦磨损性能从高到低依次是凹坑、菱形、平行、断纹；温度相同和表面微造型相同时，WS2质量分数为30％时的磨损性能要比WS2质量分数为20％时稍好一些。