响应曲面法在SPIF表面粗糙度预测及多目标优化中的应用

李韩博; 杨明顺; 李言; 姚梓萌; 徐青; 王聪聪

doi:10.13433/j.cnki.1003-8728.2017.1216

响应曲面法在SPIF表面粗糙度预测及多目标优化中的应用

doi: 10.13433/j.cnki.1003-8728.2017.1216

1.
西安理工大学机械与精密仪器工程学院, 西安 710048

基金项目:

国家自然科学基金项目（51475366）与陕西省科技计划项目（2016JM5074）资助

详细信息

作者简介:
李韩博(1988-),硕士研究生,研究方向为先进制造与现代加工技术,414463184@qq.com

通讯作者:
杨明顺(联系人),副教授,博士,Yangmingshun@xaut.edu.cn

计量
- 文章访问数: 118
- HTML全文浏览量: 17
- PDF下载量: 4
- 被引次数: 0
出版历程
- 收稿日期: 2016-09-08
- 刊出日期: 2017-12-15

Application of Responses Surface Methodology in Prediction and Multi-objective Optimization of Surface Roughness in SPIF

1.
School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an 710048, China

摘要

摘要: 以45°方锥台为研究对象，通过响应曲面分析法（RSM），利用Design-Export 8.0数学统计软件对SPIF的成形参数进行分析，采用Box-Behnken Design （BBD）实验方法，设计四因素三水平实验方案考察工具头直径D、层进给量Z、进给速度F和主轴转速S这四个因素对SPIF表面粗糙度值的影响，分别建立了SPIF纵向表面粗糙度和横向表面粗糙度的响应模型；进一步分析实际成形条件对成形参数的约束，以纵向和横向表面粗糙度整体最优，提高SPIF表面的成形质量为目标建立成形参数多目标优化模型；设计遗传算法程序并借助Matlab遗传算法工具箱进行优化问题求解。通过实验验证，该算法可以有效快速的获得满足多约束条件下的最佳成形参数。
- 单点增量成形 /
- 表面粗糙度 /
- 响应曲面法 /
- 多目标优化
Abstract: The 45° pyramidal shape is taken as the research object, the Design-Export 8.0 software of mathematical statistics via responses surface methodology(RSM) was utilized to analyze the forming parameters in SPIF. The Box-Behnken Design (BBD) experimental methods are carried out. Four factors and three levels experimental scheme is designed. The tool head diameter D, step increment Z, feed rate F and spindle speed S are the factors that influence the surface roughness in SPIF. The response models for longitudinal surface roughness and transverse surface roughness in SPIF were established respectively. The constrains of actual forming conditions upon the forming parameters are further analyzed. With the longitudinal and transverse surface roughness overall optimal, the goal of improving the forming quality of surface in SPIF, the optimization models for forming parameters were established. The genetic algorithm program is designed and the optimization problem is solved via Matlab genetic algorithm toolbox. The experiment results show that the algorithm could achieve the optimized forming parameters which satisfy multi-constraint conditions effectively and rapidly.
- single point incremental forming(SPIF) /
- surface roughness /
- responses surface methodology(RSM) /
- multi-objective optimization

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参考文献(17)

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