摘要
目的基于超声滚压后GCr15试样表面粗糙度和表面硬度与工艺参数之间的数学模型,获取超声滚压GCr15的最佳工艺参数。方法首先,通过单因素试验筛选4个工艺参数的取值范围;其次,建立基于响应曲面的超声滚压GCr15表面硬度及表面粗糙度预测模型;再次,基于遗传算法对2个预测模型进行多目标复合优化,得到最佳工艺参数;最后,针对多目标优化结果进行试验验证。结果在超声滚压处理GCr15时,滚压静压力及滚压次数对试样表面硬度及表面粗糙度的影响极显著,转速的影响不显著;进给量对表面硬度有显著影响,对表面粗糙度的影响不显著。粗糙度模型受到静压力和滚压次数双因子交互作用的影响,硬度模型不受交互作用的影响。基于遗传算法进行多目标优化得到的最佳工艺参数如下:转速为207 r/min,进给量为0.34 mm/r,静压力为0.49 MPa,滚压次数为3。在最佳工艺参数下得到试样的最低表面粗糙度为0.34μm、最高硬度为60.5HRC。结论基于响应曲面法的GCr15超声滚压表面性能预测模型准确有效。采用最优工艺参数能够获得最优表面质量。
In recent years,in order to improve the surface quality of bearings,there has been a research on the surface ultrasonic rolling technology of bearing rings and various samples made of GCr15 bearing steel in bearing manufacturing.In these studies,the analysis of the impact of rolling parameters on rolling results mostly focuses on a single surface performance index.There is a lack of analysis and summary of the impact of ultrasonic rolling parameters on the comprehensive surface quality of bearings.This paper aims to analyze the impact of rolling process parameters during ultrasonic rolling on the dual response of surface roughness and surface hardness of GCr15 specimens.Through genetic response composite optimization,the optimal combination of process parameters for ultrasonic surface rolling of GCr15 specimens was obtained.In this article,first,a single factor test was used to determine the value range for multiple impact factors.Secondly,through response surface modeling,two mathematical models of ultrasonic rolling process parameters and surface roughness and hardness of GCr15 specimens were obtained for the first time.After performing variance analysis on the mathematical models,the significance ranking of the two mathematical models and the process parameters for the two response models was obtained.Finally,this paper applied genetic algorithm to multi-objective composite optimization of two mathematical models for the first time,and obtained the optimal combination of rolling process parameters based on the two mathematical models.At the same time,this paper conducted validation tests on the parameters obtained,confirming the reliability of the optimization results.After the analysis in this article,the main results were as follows:The expressions of two second-order mathematical prediction models for surface roughness and surface hardness were determined,and the maximum error between the predicted values of the two models and the actual measured values was 9.7%.It was proved that the two models were accura
作者
黄鹏程
王燕霜
程勇杰
王高峰
袁锡铭
HUANG Pengcheng;WANG Yanshuang;CHENG Yongjie;WANG Gaofeng;YUAN Ximing(Mechanical Engineering Department,Qilu University of Technology(Shandong Academy of Sciences),Jinan 250353,China;Luoyang Bearing Research Technology Co.,Ltd.,Henan Luoyang 471039,China;Shandong Jindi Precision Machinery Technology Co.,Ltd.,Shandong Liaocheng 252035,China)
出处
《表面技术》
EI
CAS
CSCD
北大核心
2024年第5期156-165,共10页
Surface Technology
基金
国家自然科学基金(52075274)
山东省重大创新工程(2022CXGC010304)。
