Journal of Northeastern University(Natural Science) ›› 2025, Vol. 46 ›› Issue (3): 80-87.DOI: 10.12068/j.issn.1005-3026.2025.20230275

• Mechanical Engineering • Previous Articles     Next Articles

Reliability Optimization of Process Parameters Considering Milling Surface Morphology

Xian-zhen HUANG1,2(), Xu WANG1, Peng-fei DING1, Zhi-yuan JIANG1   

  1. 1.School of Mechanical Engineering & Automation,Northeastern University,Shenyang 110819,China
    2.Key Laboratory of Vibration and Control of Aero-Propulsion Systems,Ministry of Education,Northeastern University,Shenyang 110819,China. cn
  • Received:2023-09-26 Online:2025-03-15 Published:2025-05-29
  • Contact: Xian-zhen HUANG
  • About author:HUANG Xian-zhen E-mail: xzhhuang@mail.neu.edu.cn

Abstract:

Research is conducted on the ball end milling process with the aim of achieving reliable optimization of milling process parameters. Firstly, according to the motion trajectory of the ball end mill cutting edge, the surface morphology formed during machining is simulated using the Z-mapping (Z-MAP) algorithm, and the surface roughness (Ra) is introduced to measure the surface quality after machining. The accuracy of the surface morphology simulation model is validated through surface morphology analysis experiments. Then, considering the actual constraint conditions of the machining surface quality, the tool service life, and the uncertainty of process parameters during the machining process, a reliability optimization model for process parameters is established with spindle rotation speed, tool feed rate, axial cutting depth and radial cutting depth as the optimization variables, and maximizing the material removal rate (Q) as the optimization objective. Finally, the optimization model is solved using the grey wolf optimization algorithm to obtain the optimal process parameters, and the feasibility of the optimized results is verified through milling experiments.

Key words: ball end mill, Z-MAP algorithm, surface morphology, surface roughness, grey wolf optimization algorithm, reliability optimization

CLC Number: