Optimization of camshaft grinding parameters based on response surface method and NSGA2
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摘要: 为改善20CrMo钢凸轮轴磨削加工的质量和效率，基于响应曲面法进行磨削试验，分析磨削工艺参数对其表面粗糙度的影响，并建立相应的回归模型。根据工件形状特点，建立工件薄弱部位瞬时材料去除率计算模型，将表面粗糙度和材料去除率作为优化目标，利用第二代非支配快速排序遗传算法（non-dominated sorting genetic algorithm-2，NSGA2）进行多目标工艺参数组合寻优并进行试验验证。结果表明：求解得到的最优工艺参数组合是砂轮线速度为60 m/s、工件转速为96 r/min、磨削深度为30 μm，在保证工件薄弱部位表面粗糙度满足加工要求的前提下，可有效提高其磨削加工效率。
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关键词:
- 凸轮轴磨削 /
- 响应曲面法 /
- 第二代非支配快速排序遗传算法 /
- 参数优化
Abstract: In order to improve the grinding quality and efficiency of 20CrMo steel camshaft, response surface method was used to conduct grinding tests. The influence of grinding process parameters on surface roughness was analyzed, and the corresponding regression model was established. Based on the shape characteristics of the workpiece, the instantaneous material removal rate calculation model of the weak part of the workpiece was established. The model of surface roughness and material removal rate was taken as the optimization objective. The second generation of non-dominated sorting genetic algorithm was used to optimize the combination of multi-objective process parameters and test verification was carried out. The results show that the optimal combination of process parameters, namely the linear speed of the grinding wheel 60 m/s, the workpiece speed 96 r/min and the grinding depth 30 μm, can effectively improve the grinding efficiency under the premise of ensuring that the surface roughness of the weak part meets the machining requirements.-
Key words:
- camshaft grinding /
- response surface methodology /
- NSGA2 /
- parameter optimization
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表 1 磨削试验参数取值
Table 1. Parameter value of grinding test
水平 因素 砂轮线速度
vs /（m·s−1）工件转速
nw /（r·min−1）磨削深度
ap /μm−1 60 40 10 0 75 70 20 1 90 100 30 
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表 2 磨削试验方案及结果
Table 2. Grinding test scheme and results
序号 水平取值 $ \mathrm{表}\mathrm{面}\mathrm{粗}\mathrm{糙}\mathrm{度}{R}_{\mathrm{a}} $ /μm $ {v}_{\mathrm{s}} $ $ {n}_{\mathrm{w}} $ $ {a}_{\mathrm{p}} $ 1 −1 −1 0 0.151 2 1 −1 0 0.114 3 −1 1 0 0.143 4 1 1 0 0.196 5 −1 0 −1 0.166 6 1 0 −1 0.121 7 −1 0 1 0.156 8 1 0 1 0.188 9 0 −1 −1 0.127 10 0 1 −1 0.162 11 0 −1 1 0.147 12 0 1 1 0.206 13 0 0 0 0.133
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表 3 表面粗糙度回归模型方差
Table 3. Variance of regression model for surface roughness
方差来源 自由度 均方差
F值 P值 模型 9 0.009 7 20.070 0 0.015 7 $ {v}_{\mathrm{s}} $ 1 $ 1. 125\times {10}^{-6} $ 0.020 9 0.894 1 $ {n}_{\mathrm{w}} $ 1 0.003 5 65.640 0 0.003 9 $ {a}_{\mathrm{p}} $ 1 0.001 8 34.050 0 0.010 0 $ {v}_{\mathrm{s}}{n}_{\mathrm{w}} $ 1 0.002 0 37.670 0 0.008 7 $ {v}_{\mathrm{s}}{a}_{\mathrm{p}} $ 1 0.001 5 27.580 0 0.013 4 $ {n}_{\mathrm{w}}{a}_{\mathrm{p}} $ 1 0.000 1 2.680 0 0.200 2 $ {{v}_{\mathrm{s}}}^{2} $ 1 0.000 1 2.470 0 0.213 9 $ {{n}_{\mathrm{w}}}^{2} $ 1 0.000 2 4.580 0 0.121 9 $ {{a}_{\mathrm{p}}}^{2} $ 1 0.000 7 12.470 0 0.038 6 ${R^2}{\rm{ = }}0.983\;7\;\;\;\;\;R_{{\rm{adj}}}^{\rm{2}} = 0.934\;6$
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