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

• Autorzy: Zhang Ziling , Lv Xiaodong , Qi Baobao , Qi Yin , Zhang Milu , Tao Zhiqiang

Identyfikatory

DOI

10.17531/ein/183558

• Języki publikacji: EN

Abstrakty

EN

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 characteristicsof 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 thecoupling effect of various factors, a roughness prediction model isestablished by the SSA-LSSVM, and its prediction accuracy reachesmore than 95%. Then, the roughness reliability model isdevelopedby 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 surfaceroughness and roughness reliability are 9% and 1.5% respectively, indicating the correctness of the method proposed in the paper.

Słowa kluczowe

EN

surface roughness reliability; SSA-LSSVM; response surface methodology; surface quality; CNC milling

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2024
• Tom: Vol. 26, no. 2
• Strony: art. no. 183558
• Opis fizyczny: Bibliogr. 40 poz., fot., rys., tab., wykr.

Twórcy

autor

Zhang Ziling

• Logistics Engineering College, Shanghai Maritime University, Shanghai, 201306, China

• https://orcid.org/0000-0001-8132-5744

autor

Lv Xiaodong

• Logistics Engineering College, Shanghai Maritime University, Shanghai, 201306, China

autor

Qi Baobao

• 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

autor

Qi Yin

• Yingtan Advanced Technical School, Jiangxi 335000, China

autor

Zhang Milu

• Logistics Engineering College, Shanghai Maritime University, Shanghai, 201306, China

autor

Tao Zhiqiang

• College of Robotics, Beijing Union University, Beijing, 100027, China

• https://orcid.org/0000-0001-5654-4005

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