李文琴,于占江,许金凯,江海宇,于化东.基于GRA-RSM的微铣削表面质量多目标参数优化[J].表面技术,2020,49(9):370-377.
LI Wen-qin,YU Zhan-jiang,XU Jin-kai,JIANG Hai-yu,YU Hua-dong.Multi-objective Parameters Optimization of Micro-milling Surface Quality Based on GRA-RSM[J].Surface Technology,2020,49(9):370-377 |
| 基于GRA-RSM的微铣削表面质量多目标参数优化 |
| Multi-objective Parameters Optimization of Micro-milling Surface Quality Based on GRA-RSM |
| 投稿时间：2019-08-12 修订日期：2020-09-20 |
| DOI：10.16490/j.cnki.issn.1001-3660.2020.09.043 |
| 中文关键词: 表面粗糙度 残余应力 微铣削加工参数 灰色关联度 响应面法 |
| 英文关键词:surface roughness residual stress micro-milling parameters grey correlation degree response surface method |
| 基金项目:国家重点研发计划（2018YFB1107403）；中国“111”计划（D17017）；吉林省科技发展计划（20190101005JH，20180201057GX）；长春理工大学青年科学基金（XQNJJ-2018-09） |
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| Author | Institution |
| LI Wen-qin | Changchun University of Science and Technology, Changchun 130022, China |
| YU Zhan-jiang | Changchun University of Science and Technology, Changchun 130022, China |
| XU Jin-kai | Changchun University of Science and Technology, Changchun 130022, China |
| JIANG Hai-yu | Changchun University of Science and Technology, Changchun 130022, China |
| YU Hua-dong | Changchun University of Science and Technology, Changchun 130022, China |
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| 中文摘要: |
| 目的 建立表面粗糙度和残余应力的灰色关联度预测模型，确定微铣削工艺参数优化方案，在降低表面粗糙度的基础上，最大化减小残余应力。方法 首先，采用BBD试验方法设计三因素三水平微铣削试验，测量工件表面的表面粗糙度和残余应力；其次，基于灰色关联分析（Grey Correlation Analysis，GRA）方法，以表面粗糙度和残余应力的信噪比为性能指标，将多目标转化为单一目标进行优化；再次，在主成分分析的基础上，建立灰色关联分析与工艺参数之间的二阶回归预测模型；最后，利用响应面法（Response Surface Method，RSM）获得了最优参数组合。结果 构建的灰色关联度预测模型的平均误差为6.9%，优化结果提高了3.91%。实验结果表明，最优工艺参数组合为：主轴转速20 000 r/min，轴向切深60 μm，进给速度285.8 mm/min。结论 灰色关联度预测模型的拟合度良好，可靠性和准确性较高。基于GRA-RSM优化方法获得的工艺参数组合可以实现同时使表面粗糙度和残余压应力达到理想效果的最优解。 |
| 英文摘要: |
| The work aims to establish a grey correlation degree prediction model of surface roughness and residual stress and determine the optimization scheme of micro-milling process parameters, to minimize residual stress on the basis of reducing surface roughness. Firstly, a three-factor three-level micro-milling test was designed by BBD test method, and the surface roughness and residual stress of workpiece were measured. Secondly, taking the signal-to-noise ratio of surface roughness and residual stress as performance indexes, multiple targets were converted into a single target for optimization based on grey correlation analysis. Thirdly, on the basis of principal component analysis, a second-order regression prediction model between grey correlation analysis (GRA) and process parameters was established. Finally, the response surface method (RSM) was used to obtain the optimal combination of parameters. The average error of grey correlation degree prediction model was 6.9% and the optimized results were improved by 3.91%. According to the experimental results, the optimal processing parameters were as follows: the spindle speed of 20 000 r/min, the axial cutting depth of 60 μm, and the feed speed of 285.8 mm/min. Therefore, the grey correlation degree prediction model has good fitting degree and high reliability and accuracy, and the combination of process parameters based on the method proposed in this paper can achieve the optimal solution of surface roughness and residual compressive stress at the same time. |
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