黄鹏程,王燕霜,程勇杰,王高峰,袁锡铭.超声滚压GCr15表面性能模型建立及工艺参数优化[J].表面技术,2024,53(5):156-165.
HUANG Pengcheng,WANG Yanshuang,CHENG Yongjie,WANG Gaofeng,YUAN Ximing.Establishment of Surface Property Model and Optimization of Process Parameters for Ultrasonic Rolling GCr15[J].Surface Technology,2024,53(5):156-165 |
| 超声滚压GCr15表面性能模型建立及工艺参数优化 |
| Establishment of Surface Property Model and Optimization of Process Parameters for Ultrasonic Rolling GCr15 |
| 投稿时间：2023-03-13 修订日期：2023-05-20 |
| DOI：10.16490/j.cnki.issn.1001-3660.2024.05.016 |
| 中文关键词: 超声滚压 轴承钢 响应曲面法 Box-Behnken设计 遗传算法 工艺参数优化 |
| 英文关键词:ultrasonic rolling bearing steel response surface method Box-Behnken design genetic algorithm optimization of process parameters |
| 基金项目:国家自然科学基金(52075274);山东省重大创新工程(2022CXGC010304) |
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| Author | Institution |
| HUANG Pengcheng | Mechanical Engineering Department, Qilu University of Technology Shandong Academy of Sciences, Jinan 250353, China |
| WANG Yanshuang | Mechanical Engineering Department, Qilu University of Technology Shandong Academy of Sciences, Jinan 250353, China |
| CHENG Yongjie | Mechanical Engineering Department, Qilu University of Technology Shandong Academy of Sciences, Jinan 250353, China |
| WANG Gaofeng | Luoyang Bearing Research Technology Co., Ltd., Henan Luoyang 471039, China |
| YUAN Ximing | Shandong Jindi Precision Machinery Technology Co., Ltd., Shandong Liaocheng 252035, China |
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| 中文摘要: |
| 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0.34 μm、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。 |
| 英文摘要: |
| 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 accurate and effective, and could be used to predict the surface roughness and surface hardness of GCr15 samples after ultrasonic rolling treatment. The effects of ultrasonic rolling process parameters on the surface quality of GCr15 samples were obtained as follows:the static rolling pressure and rolling times had a significant impact on surface hardness and roughness; Feed rate had a significant impact on surface hardness, but had no significant impact on surface roughness; The effect of rotational speed on both responses was not significant. The roughness model was affected by the interaction of static pressure and rolling times, while the hardness model was not affected by the interaction of these two factors. The optimal process parameters obtained by multi-objective optimization based on genetic algorithm were as follows:rotational speed=207 r/min, feed rate=0.34 mm/r, static pressure=0.49 MPa, and rolling times=3 times. After verification tests, it was confirmed that the minimum surface roughness of the sample was 0.34 under the optimal parameters μ. The maximum hardness was 60.5HRC. According to the process parameters obtained by genetic algorithm, surface ultrasonic rolling of GCr15 sample could obtain the optimal surface in the comparative test. This article has important significance for the application of ultrasonic rolling technology in the optimization of bearing surface quality, and can be used as a reference for the rolling process parameters when ultrasonic rolling bearing surfaces. |
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