| 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],53(5):156-165 |
| Establishment of Surface Property Model and Optimization of Process Parameters for Ultrasonic Rolling GCr15 |
| Received:March 13, 2023 Revised:May 20, 2023 |
| View Full Text View/Add Comment Download reader |
| DOI：10.16490/j.cnki.issn.1001-3660.2024.05.016 |
| KeyWord:ultrasonic rolling bearing steel response surface method Box-Behnken design genetic algorithm optimization of process parameters |
| Author | Institution |
| HUANG Pengcheng |
Mechanical Engineering Department, Qilu University of Technology Shandong Academy of Sciences, Jinan , China |
| WANG Yanshuang |
Mechanical Engineering Department, Qilu University of Technology Shandong Academy of Sciences, Jinan , China |
| CHENG Yongjie |
Mechanical Engineering Department, Qilu University of Technology Shandong Academy of Sciences, Jinan , China |
| WANG Gaofeng |
Luoyang Bearing Research Technology Co., Ltd., Henan Luoyang , China |
| YUAN Ximing |
Shandong Jindi Precision Machinery Technology Co., Ltd., Shandong Liaocheng , China |
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| Abstract: |
| 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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