China Mechanical Engineering

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Parameter Solution of Instantaneous Milling Force Model of Variable Cross-section Scroll Plates Based on Improved PSO Algorithm

LIU Tao;ZHANG Lifang   

  1. School of Mechanical and Electrical Engineering,Lanzhou University of Technology,Lanzhou,730050
  • Online:2020-12-25 Published:2020-12-28



  1. 兰州理工大学机电工程学院,兰州,730050
  • 基金资助:

Abstract: In order to solve the multivariate nonlinear problems in the prediction of instantaneous milling forces in variable cross-section scroll plate machining processes, a mathematical model for instantaneous milling force considering the tool runout was established based on the actual milling processes, and an improved PSO algorithm was proposed to solve the mathematical model to improve the accuracy of the instantaneous milling force prediction model. The method was verified by the instantaneous milling force experiment results under four groups of milling parameters. The results show that the instantaneous milling force curves obtained by the proposed method have a high degree of agreement with the experimentally measured ones in terms of the shape and peak value, and the peak errors of the four groups of tests is within 15%. The proposed PSO algorithm could effectively improve the convergence rate of instantaneous milling force identification of variable cross-section scroll plate machining. The model parameters can be identified with higher precision and fewer trials, which reduces the test costs compared with the average milling force solving method.

Key words: variable cross-section scroll plate, instantaneous milling force, particle swarm optimization(PSO) algorithm, model parameter

摘要: 针对变截面涡旋盘瞬时铣削力预测存在的多元非线性难题,从涡旋盘实际铣削过程出发,建立了考虑刀具跳动的瞬时铣削力数学模型,提出了一种基于改进粒子群优化算法(PSO)对铣削力模型参数进行求解的方法,以提高瞬时铣削力预测模型精度。通过4组不同铣削参数下的瞬时铣削力实验对该方法进行验证,结果表明:该方法求解得到的变截面涡旋盘瞬时铣削力与实验测得的瞬时铣削力在形状和峰值处有较高的吻合度,4组实验的峰值误差在15%以内;采用自适应惯性权重和随机扰动因子的改进PSO算法能够有效地提高变截面涡旋盘瞬时铣削力系数辨识的收敛速度和收敛效果,还能提高算法整体搜索能力。该方法只需较少的实验次数就能辨识出较高精度的模型参数,比平均铣削力求解方法的实验成本更低,对涡旋盘的加工具有重要参考价值。

关键词: 变截面涡旋盘, 瞬时铣削力, 粒子群优化算法, 模型参数

CLC Number: