3D Roughness Prediction Modeling and Evaluation of Textured Liner of Piston Component-Cylinder System  

作　　者：Yanjun Lü Cheng Liu Yongfang Zhang Cheng Jiang Xudong Bai Zhiguo Xing 

机构地区：[1]School of Mechanical and Precision Instrument Engineering,Xi’an University of Technology,Xi’an 710048,China [2]State Key Laboratory for Manufacturing Systems Engineering,Xi’an Jiaotong University,Xi’an 710049,China [3]School of Printing,Packaging Engineering and Digital Media Technology,Xi’an University of Technology,Xi’an 710054,China [4]National Key Laboratory for Remanufacturing,Army Academy of Armored Forces,Beijing 100072,China

出　　处：《Chinese Journal of Mechanical Engineering》2024年第5期203-215,共13页中国机械工程学报（英文版）

基　　金：Supported by National Natural Science Foundation of China(Grant No.52075438);Key Research and Development Program of Shaanxi Province of China(Grant No.2024GX-YBXM-268);Open Project of State Key Laboratory for Manufacturing Systems Engineering of China(Grant No.sklms2020010).

摘　　要：In this study,a machine vision method is proposed to characterize 3D roughness of the textured surface on cylinder liner processed by plateau honing.The least absolute value(L∞)regression robust algorithm and Levenberg-Marquardt(LM)algorithm are employed to reconstruct image reference plane.On this basis,a single-hidden layer feedforward neural network(SLFNN)based on the extreme learning machine(ELM)is employed to model the relationship between high frequency information and 3D roughness.The characteristic parameters of Abbott-Firestone curve and 3D roughness measured by a confocal microscope are used to construct ELM-SLFNN prediction model for 3D roughness.The results indicate that the proposed method can effectively characterize 3D roughness of the textured surface of cylinder liner.

关 键 词：Surface texture Cylinder liner 3D roughness Neural network 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TG84[自动化与计算机技术—计算机科学与技术]