Surface roughness prediction and roughness reliability evaluation of CNC milling based on surface topography simulation

被引:4
作者
Zhang, Ziling [1 ]
Lv, Xiaodong [1 ]
Qi, Baobao [2 ,3 ]
Qi, Yin [4 ]
Zhang, Milu [1 ]
Tao, Zhiqiang [5 ]
机构
[1] Shanghai Maritime Univ, Logist Engn Coll, Shanghai 201306, Peoples R China
[2] Jilin Univ, Key Lab CNC Equipment Reliabil, Minist Educ, Changchun 130000, Peoples R China
[3] Key Lab Adv Mfg & Intelligent Technol High End CNC, Jilin 130000, Peoples R China
[4] Yingtan Adv Tech Sch, Yingtan 335000, Jiangxi, Peoples R China
[5] Beijing Union Univ, Coll Robot, Beijing 100027, Peoples R China
来源
EKSPLOATACJA I NIEZAWODNOSC-MAINTENANCE AND RELIABILITY | 2024年 / 26卷 / 02期
基金
中国国家自然科学基金;
关键词
surface roughness reliability; SSA-LSSVM; response surface methodology; surface quality; CNC milling; MODEL; WEAR;
D O I
10.17531/ein/183558
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Surface roughness is influenced by various factors with uncertainty characteristic, and roughness reliability can be used for the assessment of the surface quality of CNC milling. The paper develops a method for the assessment of surface quality by considering the coupling effect and uncertainty characteristics of various factors. According to the milling kinematics theory, the milling surface topography simulation is conducted by discretizing the cutting edge, machining time, and workpiece. Considering the coupling effect of various factors, a roughness prediction model is established by the SSA-LSSVM, and its prediction accuracy reaches more than 95%. Then, the roughness reliability model is developed by applying the response surface methodology to achieve the assessment of surface quality. The proposed method is verified by the milling experiments. The maximum values of the relative errors between the simulation and experimental results of the surface roughness and roughness reliability are 9% and 1.5% respectively, indicating the correctness of the method proposed in the paper.
引用
收藏
页数:20
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