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Surface roughness prediction and roughness reliability evaluation of CNC milling based on surface topography simulation
Baobao Qi 2,3
Yin Qi 4
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Logistics Engineering College, Shanghai Maritime University, Shanghai, 201306, China
Key Laboratory of CNC Equipment Reliability, Ministry of Education, Jilin University,130000, China
Key Laboratory of Advanced Manufacturing and Intelligent Technology for High-end CNC Equipment,130000, China
Yingtan Advanced Technical School, Jiangxi 335000, China
College of Robotics, Beijing Union University, Beijing, 100027, China
Submission date: 2023-10-26
Final revision date: 2023-12-24
Acceptance date: 2024-01-31
Online publication date: 2024-02-11
Publication date: 2024-02-11
Corresponding author
Ziling Zhang   

Logistics Engineering College, Shanghai Maritime University, Shanghai, 201306, China
  • A milling surface topography model was developed based on milling kinematics theory.
  • A surface roughness prediction model is proposed based on the sparrow search algorithm optimized least support square vector machine method.
  • A surface roughness reliability model is presented based on response surface methodology.
  • The correctness of the proposed method is verified by milling experiments on 7050 aluminium alloy.
Surface roughness is influenced by various factors with uncertainty characteristics, 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 characteristic of various factors. According to the milling kinematics theory, the milling surface topography simulation was 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 was 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.
The research was sponsored by the National Natural Science Foundation of China (grant no.51905334, grant no 52305261 and grant no.12002186), Shanghai Sailing Program (grant no.19YF1418600), Beijing Union University (No. ZK80202101), R&D Program of Beijing Municipal Education Commission (KM202211417012) and the Open Foundation of National Key Laboratory of Strength and Structural Integrity (ASSIKFJJ202305002).
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