文献类型：期刊文献

中文题名：硬质合金微坑车刀切削304不锈钢时的表面粗糙度研究

英文题名：Research on Surface Roughness of 304 Stainless Steel Cut by Cemented Carbide Micro Pit Tool

作者：袁森 何林 占刚 蒋宏婉 邹中妃

第一作者：袁森

通信作者：He, Lin

机构：[1]贵州大学机械工程学院 贵阳550025;贵州理工学院机械工程学院 贵阳550003;[2]贵州大学机械工程学院 贵阳550025;六盘水师范学院 六盘水550018;[3]贵州大学;[4]贵州理工学院

第一机构：贵州理工学院机械工程学院

年份：2018

卷号：0

期号：15

起止页码：232-240

中文期刊名：机械工程学报

外文期刊名：Journal of Mechanical Engineering

收录：CSTPCD;;EI(收录号：20184406012350);Scopus;北大核心:【北大核心2017】；CSCD:【CSCD2017_2018】；

基金：国家自然科学基金资助项目(51765009,51665007)

语种：中文

中文关键词：微坑车刀;表面粗糙度;预测模型;304不锈钢

外文关键词：micro pit turning tool;surface roughness;prediction model;304 stainless steel

摘要：304不锈钢因其良好的工作性能和难加工性,成为金属切削加工领域的研究热点尤其是良好的加工表面质量的获取及其控制广受业界关注.基于自主研发的切削性能良好的304不锈钢专用硬质合金微坑车刀,重点研究该微坑车刀切削304不锈钢表面粗糙度的特性.通过对比试验,研究原车刀和微坑车刀切削304不锈钢棒料时的表面粗糙度变化,揭示出微坑车刀切削304不锈钢的表面粗糙度降低机理.利用响应曲面试验,分析切削参数对表面粗糙度的单因素和交互影响规律,建立微坑车刀表面粗糙度预测模型.研究结果表明,微坑车刀相比较普通车刀在切削过程中具有较小的切削力,是导致微坑车刀加工304不锈钢表面粗糙度较低的主要原因;所建立的表面粗糙度预测模型具有较高可靠性,可用于切削参数优化;获得的优选切削参数方案,与实际生产推荐的参数相比,在优选切削参数下获得的表面粗糙度降幅达42.47％.
Because of its service excellent performance and difficult processing, the 304 stainless steel has always been a hot research spot, especially on the acquirement and control of its good surface quality for processing. In order to study the characteristics of surface roughness of 304 stainless steel cut by self designed micro pit turning tool, It discussed the comparison experimental study on the surface roughness of 304 stainless steel bar cut by the common turning tool and micro pit turning tool respectively, and the mechanism of surface roughness reduction cut by micro pit turning tool is explained. By response surface experiments, the prediction model of surface roughness is established; the single factor and interaction effect of cutting parameters on the surface roughness is analyzed. The results show that the cutting force of micro pit turning tool is lower than that of common turning tool in the cutting process, which is the main reason for reducing the surface roughness. The surface roughness prediction model is with good reliability, the optimized cutting parameters acquired in the experiment can decrease the surface roughness by a percentage of up to 42.47% compared to the practical cutting parameters.