| 摘要: |
| 活塞-缸套系统是内燃机重要的摩擦副之一，活塞-缸套的表面质量影响着活塞-缸套系统的摩擦学性能，进而直接影响整机的服役性能。针对珩磨缸套表面 2D 粗糙度参数的局限性与表面粗糙度非接触检测方法研究，提出一种基于广义回归神经网络(GRNN)的珩磨缸套表面 3D 粗糙度图像检测方法。通过运用灰度共生矩阵(GLCM)提取缸套表面图像的纹理特征参数，并分析纹理特征参数与 3D 粗糙度间的相关性。以图像纹理特征参数作为输入，分别采用 GRNN 和多元回归分析 (MRA)建立 3D 粗糙度检测模型，通过与试验检测结果对比验证了模型的准确性。GRNN 检测模型获得的可决系数 R2均值 (0.962)优于 MRA 检测模型，且均方误差 MSE 均值(0.07)更小，与试验检测结果对比可知，采用 GRNN 建立的珩磨缸套 3D 粗糙度检测模型具有更高的精度，与实测 3D 粗糙度的相对误差均值为 7.9%。所建立的 3D 粗糙度检测模型具有较高的检测精度。 |
| 关键词: 3D 粗糙度 粗糙度检测 回归分析 广义回归神经网络 |
| DOI：10.11933/j.issn.1007?9289.20220328001 |
| 分类号:TH117 |
| 基金项目:国家自然科学基金(52075438，51775428)、陕西省重点研发计划(2020GY-106)和机械制造系统工程国家重点实验室开放课题(sklms2020010)资助项目 |
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| Image Detection Method for 3D Surface Roughness of Honed Cylinder Liner Based on GRNN |
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LÜ Yanjun1, JIANG Cheng1, ZHANG Yongfang2, XING Zhiguo3, ZHAO Xiaowei1, LUO Hongbo1
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1.School of Mechanical and Precision Instrument Engineering, Xi’ an University of Technology,Xi’ an 710048 , China;2.School of Printing, Packaging Engineering and Digital Media Technology, Xi’ an University of Technology,Xi’ an 710054 , China;3.National Key Laboratory for Remanufacturing, Academy of Army Armored Forces, Beijing 100072 , China
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| Abstract: |
| The piston-cylinder liner system is one of the important friction pairs of the internal combustion engines, and surface quality of the piston-cylinder liner affects the tribological performance of the piston-cylinder liner system, which directly affects the service performance of the whole machine. Aiming at the limitation of 2D roughness parameters of honing cylinder liner surface and non-contact detection method of surface roughness, an image detection method for 3D surface roughness of honed cylinder liner based on generalized regression neural network (GRNN) is proposed. The gray level co-occurrence matrix (GLCM) is employed to extract the texture feature parameters of the cylinder liner surface image. Five texture feature parameters of GLCM are selected as representative and independent texture feature parameters to evaluate the surface topography and 3D roughness of the cylinder liner:Energy, Contrast, Correlation, Inverse Difference Moment and Entropy, and the correlation between texture feature parameters and 3D roughness is analyzed. On this basis, with the image texture feature parameters as input, the 3D roughness detection models based on GRNN and multiple regression analysis (MRA) are established respectively, and the accuracy of the models are verified by comparison with the measured results. The mean of R2 (0.962) obtained by the GRNN detection model is better than the MRA detection model, and the mean of MSE (0.07) is smaller. Compared with the measured results, it can be seen that the 3D roughness detection model of the honed cylinder liner based on GRNN has higher accuracy, and the average relative error with the measured 3D roughness is 7.9%. The results show that the established 3D roughness detection model has high detection accuracy. |
| Key words: three-dimensional roughness roughness detection multiple regression analysis generalized regression neural network |
