叶片抛磨表面粗糙度优化预测模型及实验研究

Optimal Prediction Model and Experimental Research on Blade Polishing Surface Roughness

抛磨作为提高叶片表面质量的最后一道工序,能够显著提高叶片表面完整性,表面粗糙度是衡量叶片抛磨后表面完整性最重要的技术指标。采用六自由度机器人+百叶轮弹性磨具对叶片进行抛磨加工,首先采用单因素实验法分析了影响叶片表面粗糙度的主要工艺参数,接着采用正交试验得出了叶片抛磨加工的优化工艺参数区间,最后采用非线性回归模型对表面粗糙度进行了预测。实验验证结果表明,影响叶片表面粗糙度的主要工艺参数依次为百叶轮目数、接触压缩量、抛磨循环次数和机器人进给速度,采用川崎RS20N机器人抛磨某型号精铸汽轮机叶片,优选区间为百叶轮目数(200~600)#之间,接触压缩量为(0.2~1.2)mm,抛磨循环次数为(2~4)次,进给速度为(0.1~0.4)mm/s,在优选工艺区间进行加工,表面粗糙度均低于0.4μm,预测模型和实际抛磨结果误差率低于10%,表明该预测模型能够为实现叶片抛磨工艺参数在线控制和调整提供理论依据。

As the last process to improve the surface quality of the blade,polishing can significantly improve the surface integrity of the blade. Surface roughness is the most important technical index to measure the surface integrity of the blade after polishing.Therefore,a six-degree-of-freedom robot + louver elastic abrasive tool is used to polish the blades. First,the single factor experiment method is used to analyze the main process parameters that affect the surface roughness of the blade,and then the orthogonal test is used to obtain the blade polishing the optimization process parameter interval of processing,and finally the surface roughness is predicted by the nonlinear regression model.The experimental verification results show that the main process parameters that affect the surface roughness of the blade are the number of louver meshes,contact compression,the number of polishing cycles and robot feed speed. Use Kawasaki RS20N robot to polish a certain model of precision cast steam turbine blades.The optimization interval is louver mesh number 200#-600#,contact compression amount is(0.2~1.2)mm,the number of polishing cycles is(2~4)times,the feed speed is(0.1~0.4)mm/s,and the surface roughness is lower than 0.4μm in the optimized process interval. The prediction model and the error rate of actual polishing results is less than 10%,indicating that the prediction model can provide a theoretical basis for realizing online control and adjustment of blade polishing process parameters.