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超声滚挤压风电轴承材料表面粗糙度加工参数敏感性研究 被引量:7

Sensitivity study on surface roughness processing parameters for wind turbine bearing materials by ultrasonic rolling extrusion
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摘要 为了确定给定范围内的超声滚挤压风电轴承材料表面粗糙度加工参数的最优区间,以42CrMo钢风电轴承材料试样为研究对象,开展超声滚挤压表面粗糙度试验,基于试验结果构建表面粗糙度指数函数预测模型,分析加工参数对表面粗糙度及其灵敏度的影响,确定加工参数的稳定域和非稳定域,优选出最佳的超声滚挤压42CrMo钢表面粗糙度加工参数区间。研究结果表明:加工参数对表面粗糙度的影响程度大小依次为静压力F_(s)、工件转速n、进给速度f、振幅A。其中,n的优选区间为500~600 r·min^(-1),f的优选区间为35~45 mm·min^(-1),A的优选区间为15~20μm,F_(s)的优选区间为400~500 N。 In order to obtain the optimal range of surface roughness processing parameters for wind turbine bearing materials by the ultrasonic rolling extrusion within a given range, for wind turbine bearing materials sample made of 42 CrMo steel, the ultrasonic rolling extrusion the surface roughness test was conducted, and the surface roughness exponential function prediction model was constructed based on the test result. Then, the influences of the processing parameters on the surface roughness and the sensitivity were analyzed, the stable and non-stable regions of processing parameters were determined, and the best ranges of surface roughness processing parameters for ultrasonic rolling extrusion 42 CrMo steel were selected. The results show that the influence degree of the processing parameters on the surface roughness is static pressure F_(s), rotate speed n of workpiece, feeding speed f and amplitude A, and the optimal range of n, f, A and F_(s) is 500-600 r·min^(-1), 35-45 mm·min^(-1), 15-20 μm and 400-500 N, respectively.
作者 任雁 刘佳 刘斌 王晓强 Ren Yan;Liu Jia;Liu Bin;Wang Xiaoqiang(Henan Orchard Management Special Robot Engineering Technology Research Center,Luoyang 471003,China;Department of Information and Art Design,Henan Forestry Vocational College,Luoyang 471003,China;School of Mechatronics Engineering,Henan University of Science and Technology,Luoyang 471003,China)
出处 《锻压技术》 CAS CSCD 北大核心 2022年第1期98-105,共8页 Forging & Stamping Technology
基金 国家自然科学基金资助项目(U1804145)。
关键词 超声滚挤压 42CRMO钢 表面粗糙度 指数函数预测模型 参数敏感性 加工参数区间 ultrasonic rolling extrusion 42CrMo steel surface roughness exponential function prediction model parameter sensitivity processing parameter range
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