中国机械工程

• 智能制造 • 上一篇    下一篇

基于近似模型的铣削工艺参数可靠性设计优化

尚宝平1;闫富宏1;刘高峰2;陈振中3;邱浩波4;李晓科1   

  1. 1.郑州轻工业大学河南省机械装备智能制造重点试验室,郑州,450002
    2.洛阳天浩泰轨道装备制造有限公司,洛阳,471000
    3.东华大学机械工程学院,上海,201620
    4.华中科技大学机械科学与工程学院,武汉,430074
  • 出版日期:2019-02-25 发布日期:2019-02-26
  • 基金资助:
    国家自然科学基金资助项目(51675198, 5145302);
    河南省高等学校重点科研项目(19A460031)

Reliability-based Design Optimization of Milling Process Parameters Using Approximate Model

SHANG Baoping1;YAN Fuhong1;LIU Gaofeng2;CHEN Zhenzhong3;QIU Haobo4;LI Xiaoke1   

  1. 1.Henan Key Laboratory of Intelligent Manufacturing of Mechanical Equipment,Zhengzhou University of Light Industry, Zhengzhou, 450002
    2.Luoyang Tihot Rail Equipment Manufacturing Co., Ltd., Luoyang, Henan, 471000
    3. College of Mechanical Engineering, Donghua University, Shanghai, 201602
    4. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074
  • Online:2019-02-25 Published:2019-02-26

摘要: 针对现有铣削工艺参数优化方法未考虑设计参数不确定性,导致优化结果难以满足实际产品性能要求的问题,引入近似模型对铣削工艺参数进行可靠性设计优化。以铣削加工表面粗糙度为目标函数,以最大铣削力小于给定值的可靠度作为约束,综合考虑铣削加工过程中铣削速度和每齿进给量的变动,建立了铣削工艺参数可靠性优化模型,并分别采用Kriging近似和径向基函数近似对铣削表面粗糙度、铣削力与设计变量之间的隐式关系进行近似替代,最后采用Monte Carlo仿真-序列近似规划对模型进行了寻优求解,通过试验对可靠性优化的结果进行了验证。结果表明,该方法可有效地降低铣削加工表面粗糙度,并且可保证加工过程中最大铣削力的可靠度要求。

关键词: 铣削加工, 工艺参数优化, 可靠性优化, 近似模型

Abstract: The uncertainties of design parameters were not considered in the existing milling process parameter optimization methods, resulting in the optimization results difficult to meet the performance requirements of the actual products. The reliability-based design optimization method was used to determine the optimal milling process parameters. In the proposed method, the surface roughness was the objective function, and the reliability that the maximum milling force less than the given value was taken as the constraint. Considering the variations of milling speeds and feeds per tooth, a reliability optimization model of milling process parameters was established, Kriging approximation and radial basis function approximation were used to replace the implicit relationships of milling surface roughness, milling force and design variables. Finally, Monte Carlo simulation-sequential approximation programming was used to solve the optimal design of the proposed model. The reliability optimization results were verified through milling experiments. The results show that this method may effectively reduce the milling surface roughness and ensure the reliability requirements of the maximum milling force.

Key words: milling, process parameter optimization, reliability optimization, approximate model

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