Prediction and optimization of robot processing technology based on neural network and genetic algorithm
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摘要: 以KUKA KR60L30HA型工业机器人加工砂岩为例，基于BP神经网络和遗传算法进行机器人加工磨削力的预测和磨削工艺参数的优化。首先，采用正交试验法，分析加工工艺参数对磨削力信号的影响规律；其次，采用BP神经网络进行机器人加工磨削力预测模型训练并进行预测；最后，采用遗传算法对磨削加工工艺参数进行优化。结果表明：磨削工艺参数对3个磨削力分量和磨削合力的影响主次顺序不同，基本上都随径向切深ae、轴向切深ap、进给速度vw的增加呈增长趋势，随主轴转速n的增加呈下降趋势；基于BP神经网络建立的预测模型具有较好的预测精度和稳定性，符合预测要求；同时，采用遗传算法得到的优化磨削工艺参数组合是ae = 2.28 mm，ap = 2.98 mm，n = 9 586.65 r/min，vw = 2 207.67 mm/min，此时的材料去除率预测值RMRRP = 14 999.79 mm3/min，材料去除率试验值RMRRT = 14 194.44 mm3/min，试验值相对预测值的相对误差为−5.37%。Abstract:
Objectives With the rapid development of industrial automation and intelligent manufacturing, the application of industrial robots in the stone processing industry has garnered increasing attention. However, compared to other advanced manufacturing sectors, the mechanization, automation and intelligence of the stone processing industry remain relatively underdeveloped. This study aims to explore the optimal processing methods for stone industrial robots' grinding operations using BP neural networks and genetic algorithms, taking the processing of sandstone as an example. Methods Taking the KUKA KR60L30HA industrial robot equipped with a brazed flat grinding head as the representative, the effects of different grinding process parameters on grinding force signals were systematically analyzed by the orthogonal test method. Firstly, the grinding force signal data were collected using different grinding test settings. Subsequently, a three-layer grinding force prediction model based on a BP neural network was established, and linear regression analysis was conducted using the orthogonal experimental data as samples to compare the predicted values with the experimental values. Finally, the genetic algorithm was applied to optimize grinding process parameters with material removal rates as the indicator. Results The grinding process parameters have significant effects on grinding forces, but the order of major and secondary effects of different parameters varies with grinding force components. The order of influence on tangential grinding force is the axial cutting depth ap, followed by radial cutting depth ae, the feed rate vw and the spindle speed n, while the order of influence on normal grinding force is ap, vw, ae and n. In contrast, the order of influence of axial grinding force is n, ae, vw and ap, while the total grinding force is most affected by vw, in the order of vw, ae, n and ap. Additionally, all components of grinding force generally increase with the rise of ae, ap and vw, and decrease with the increase of spindle speed n. As the radial cutting depth ae increases, the ratio of normal to tangential grinding force shows a continuous downward trend. As the axial cutting depth ap increases, the grinding force ratio fluctuates within a certain range. As the spindle speed n increases, the grinding force ratio first increases, then decreases, and then slightly increases. When the feed rate vw increases, the grinding force ratio shows an initial decrease followed by an increasing trend. After training and predicting using the BP neural network model, the predicted values of tangential, normal and axial grinding forces are compared with the experimental data. The maximum absolute relative error of the axial grinding force is 7.84%, and the correlation coefficient of the model is as high as 0.998 09, indicating significant prediction accuracy of the mpdel. The optimal process parameter combination determined through genetic algorithm optimization is a radial cutting depth of 2.28 mm, an axial cutting depth of 2.98 mm, a spindle speed of 9 586.65 r/min and a feed rate of 2 207.67 mm/min. Under the optimal process parameter combination, the predicted material removal rate of the workpiece is 14 999.79 mm3/min, with a relative error of −5.37% compared to the actual experimental value of 14 194.44 mm3/min. This further demonstrates the effectiveness of the proposed optimization strategy. Conclusions The constructed grinding force prediction and process parameter optimization model has achieved systematic analysis and optimization of grinding force in robot sandstone processing. This model can clearly reveal the role of various grinding process parameters in machining and reflect their importance in improving machining efficiency and material removal rate. The changing trend of grinding force varies with different processing conditions, and there are significant differences in the influences of different parameter combinations on grinding forces, especially the influences of axial cutting depth and the feed rate, which are particularly significant. The optimal process parameter combination for material removal rate in stone processing is determined through BP neural network and genetic algorithm, and the relative error between the predicted value and the experimental value is relatively small. -
图 3 磨削力随加工工艺参数变化的趋势
Figure 3. Trend of grinding force changing with processing parameters
图 4 磨削力比随加工工艺参数变化的趋势
Figure 4. Trend of grinding force ratio changing with processing parameters
图 6 磨削力的预测值和试验值对比图
Figure 6. Comparison plots of predicted and experimental values of grinding forces
表 1 磨削力的正交试验因素及水平
Table 1. Orthogonal test factors and levels of grinding force
水平 因素 径向切深
ae / mm
A轴向切深
ap / mm
B主轴转速
n / (r·min−1)
C进给速度
vw / ( mm·min−1)
D1 2 1.0 5 000 500 2 3 1.5 6 000 1 000 3 4 2.0 7 000 1 500 4 5 2.5 8 000 2 000 5 6 3.0 9 000 2 500 
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表 2 磨削力正交试验数据及极差分析结果
Table 2. Orthogonal experimental data and range analysis results of grinding forces
序号 A B C D 切向磨削力
Ft / N法向磨削力
Fn / N轴向磨削力
Fa / N磨削合力
F合 / N法向和切向
磨削力比Kf材料去除率
RMRR / (mm3·min−1)1 2 1.0 5 000 500 12.10 12.59 39.83 41.93 1.04 1.00 × 103 2 2 1.5 6 000 1 000 30.89 32.94 49.82 61.79 1.07 3.00 × 103 3 2 2.0 7 000 1 500 32.65 34.93 36.05 53.23 1.07 6.00 × 103 4 2 2.5 8 000 2 000 40.57 43.59 33.32 59.70 1.07 10.00 × 103 5 2 3.0 9 000 2 500 48.55 52.92 11.78 64.44 1.09 15.00 × 103 6 3 1.0 6 000 1 500 16.59 14.83 39.14 42.89 0.89 4.50 × 103 7 3 1.5 7 000 2 000 45.56 40.67 61.57 78.22 0.89 9.00 × 103 8 3 2.0 8 000 2 500 71.34 63.60 73.6 105.57 0.89 15.00 × 103 9 3 2.5 9 000 500 27.15 24.36 20.38 35.11 0.90 3.75 × 103 10 3 3.0 5 000 1 000 64.88 57.65 38.59 79.14 0.89 9.00 × 103 11 4 1.0 7 000 2 500 36.68 38.50 40.87 56.59 1.05 10.00 × 103 12 4 1.5 8 000 500 21.46 17.72 32.41 38.90 0.83 3.00 × 103 13 4 2.0 9 000 1 000 46.78 39.78 51.96 69.92 0.85 8.00 × 103 14 4 2.5 5 000 1 500 121.43 96.06 82.76 147.82 0.79 15.00 × 103 15 4 3.0 6 000 2 000 143.56 108.88 68.30 161.44 0.76 24.00 × 103 16 5 1.0 8 000 1 000 23.37 19.53 39.76 46.38 0.84 5.00 × 103 17 5 1.5 9 000 1 500 48.45 38.67 66.55 82.67 0.80 11.25 × 103 18 5 2.0 5 000 2 000 164.93 119.23 163.9 232.69 0.72 20.00 × 103 19 5 2.5 6 000 2 500 227.37 165.41 151.50 273.57 0.73 31.25 × 103 20 5 3.0 7 000 500 58.23 50.03 38.07 70.97 0.86 7.50 × 103 21 6 1.0 9 000 2 000 34.05 26.80 53.53 64.63 0.79 12.00 × 103 22 6 1.5 5 000 2 500 124.19 80.59 174.40 215.62 0.65 22.50 × 103 23 6 2.0 6 000 500 45.00 45.49 63.48 82.59 1.01 6.00 × 103 24 6 2.5 7 000 1 000 93.31 75.93 75.99 125.78 0.81 15.00 × 103 25 6 3.0 8 000 1 500 146.63 107.90 89.36 177.41 0.74 27.00 × 103 切向
磨削力
Ft / NKi1 32.95 24.56 97.51 32.79 ap > ae > vw > n Ki2 45.10 54.11 92.68 51.84 Ki3 73.98 72.14 53.28 73.15 Ki4 104.47 101.97 60.68 85.73 Ki5 88.64 92.37 41.00 101.63 Ri 71.52 77.41 56.51 68.84 法向
磨削力
Fn / NKi1 35.40 22.45 73.22 30.04 ap > vw > ae > n Ki2 40.22 42.12 73.51 45.17 Ki3 60.19 60.61 48.01 58.48 Ki4 78.58 81.07 50.47 67.83 Ki5 67.34 75.48 36.51 80.20 Ri 43.18 58.62 37.00 50.17 轴向
磨削力
Fa / NKi1 34.16 42.63 99.90 38.83 n > ae > vw > ap Ki2 46.66 76.95 74.45 51.22 Ki3 55.26 77.80 50.51 62.77 Ki4 91.96 72.79 53.69 76.12 Ki5 91.35 49.22 40.84 90.43 Ri 57.80 35.17 59.06 51.60 磨削
合力
F合 / NKi1 56.22 50.48 143.44 53.90 vw > ae > n > ap Ki2 68.18 95.44 124.46 76.60 Ki3 94.93 108.80 76.96 100.80 Ki4 141.26 128.40 85.59 119.33 Ki5 133.21 110.68 63.35 143.16 Ri 85.04 77.91 80.09 89.26 法向和
切向磨
削力比
KfKi1 1.068 0.922 0.819 0.927 ae > n > vw > ap Ki2 0.893 0.846 0.892 0.891 Ki3 0.855 0.909 0.937 0.858 Ki4 0.789 0.861 0.873 0.847 Ki5 0.799 0.866 0.885 0.882 Ri 0.280 0.075 0.119 0.080
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表 3 神经网络预测值和试验值比较
Table 3. Comparison between neural network predicted values and experimental values
序号 A B C D 对比项目 Ft Fn Fa F合 1 6 2.5 7 000 2 500 试验值 / N 238.24 166.19 198.10 351.60 预测值 / N 235.03 163.42 177.17 325.18 相对误差 / % −1.35 −1.67 −10.57 −7.51 2 6 1.5 8 000 2 500 试验值 / N 83.22 48.48 83.11 127.21 预测值 / N 72.82 45.99 87.50 116.10 相对误差 / % −12.50 −5.14 5.28 −8.73
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