Optimization Method of Processing Parameters in Laser Cladding by Integrating Response Surface Methodology and Particle Swarm Optimization
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摘要: 为制备高质量的熔覆层, 保证再制造机械零部件再服役寿命, 提出了一种响应面法与粒子群算法集成的激光熔覆工艺参数优化方法。该方法以熔覆层质量为优化目标, 以工艺参数为优化变量, 基于实验结果构建工艺参数与熔覆层质量间的响应面近似数学模型, 使用粒子群算法对优化问题进行求解得到最优工艺参数组合。最后通过激光多道熔覆实验进行验证, 结果表明该方法优化后的工艺参数能够有效改善熔覆层质量, 进而节约实验成本、提高生产效率。Abstract: In order to prepare high-quality cladding layers and ensure the re-service life of remanufactured mechanical parts, a processing parameters optimization method in laser cladding by integrating response surface methodology and particle swarm optimization is proposed. The method takes the quality of the cladding layer as the optimization goal, and takes the processing parameters as the optimization variables, and the response surface approximate model between the process parameters and the quality of cladding layer according to the experimental results is built, and the particle swarm algorithm is used to obtain the optimal processing parameters combination. Finally, it was verified with the laser multi-pass cladding experiments. The results show that the optimized processing parameters of this method can effectively improve the quality of the cladding layer, thereby saving experimental costs and improving production efficiency.
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表 1 45钢与M2各元素的质量分数
% 材料 Fe C Mn Si Cr V W Mo Ni Cu 45钢 Bal 0.46 0.65 0.27 0.17 - - - 0.24 - M2 Bal 0.85 0.25 0.4 4.0 2.10 6.40 5.0 0.26 0.20 
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表 2 激光熔覆工艺参数与编码水平数
工艺因素 水平(编码及真实值) -2 -1 0 +1 +2 A/W 2 400 2 430 2 460 2 490 2 520 B/(mm·s-1) 10 11 12 13 14 C/(r·min-1) 1.7 1.8 1.9 2.0 2.1 D/% 35 40 45 50 55
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表 3 实验方案与实验结果
序号 A B C D W/mm H/mm Sc/mm2 Sp/mm2 F D 实验结果 1 0 -2 0 0 10.389 1.216 8.393 4.378 0.664 4 0.342 8 
2 -1 1 -1 1 9.455 1.140 7.748 2.888 0.719 1 0.271 5 3 0 0 0 0 10.184 1.121 8.343 3.489 0.731 1 0.294 9 4 -1 -1 1 1 9.793 0.306 2.234 2.771 0.746 5 0.553 7 5 -1 -1 -1 -1 10.930 1.292 9.977 4.314 0.706 3 0.301 9 6 -1 1 1 -1 10.746 1.006 7.870 3.799 0.728 0 0.325 5 7 0 0 0 0 10.048 1.127 8.359 3.899 0.738 2 0.318 1 8 1 -1 -1 -1 10.604 1.190 9.156 3.969 0.725 3 0.302 4 9 0 0 0 -2 10.750 0.993 8.220 2.939 0.769 8 0.263 3 10 -1 -1 -1 1 9.640 1.344 9.376 3.288 0.723 9 0.259 6 11 0 0 -2 0 9.870 1.038 7.162 4.391 0.699 2 0.380 1 12 2 0 0 0 10.196 0.376 3.038 3.654 0.793 3 0.546 0 13 0 0 0 2 8.870 1.350 8.880 2.659 0.741 8 0.230 4 14 1 1 1 -1 10.633 0.980 7.664 3.583 0.735 2 0.318 6 15 -2 0 0 0 10.191 1.095 8.424 3.502 0.754 8 0.293 6 16 -1 -1 1 -1 10.010 1.114 7.910 3.035 0.709 3 0.277 3 17 -1 1 -1 -1 9.420 1.235 8.586 2.450 0.738 0 0.222 0 18 1 1 -1 -1 10.163 1.407 10.556 2.839 0.738 2 0.211 9 19 -1 1 1 1 10.048 1.121 7.741 3.693 0.687 4 0.323 0 20 1 1 1 1 9.235 1.153 8.158 2.454 0.766 5 0.231 2 21 0 0 0 0 10.017 1.318 9.606 2.358 0.727 8 0.197 1 22 0 0 2 0 10.105 1.356 10.230 2.053 0.746 6 0.167 1 23 0 2 0 0 9.991 0.993 7.674 3.127 0.773 3 0.289 5 24 0 0 0 0 10.113 1.318 9.539 3.190 0.715 7 0.250 6 25 0 0 0 0 10.300 1.388 9.807 3.216 0.686 0 0.246 9 26 1 -1 1 -1 10.665 1.458 11.013 2.969 0.708 3 0.212 4 27 1 -1 -1 1 9.838 1.566 10.668 3.476 0.692 4 0.245 8 28 1 1 -1 1 9.558 1.114 7.859 2.653 0.738 0 0.252 4 29 1 -1 1 1 9.807 1.446 10.402 2.725 0.733 4 0.207 6 30 0 0 0 0 10.078 1.222 8.905 2.833 0.723 0 0.241 4
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表 4 表面平整度模型方差分析结果
方差来源 平方和 自由度 均方 F值 P值 模型 0.020 1 19 0.001 1 14.25 < 0.000 1 显著 残差 0.000 7 10 0.000 1 失拟项 0.000 4 5 0.000 1 0.918 0.536 2 不显著 纯误差 0.000 4 5 0.000 1 总和 0.020 8 29
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表 5 稀释率模型方差分析结果
方差来源 平方和 自由度 均方 F值 P值 模型 0.1859 15 0.0124 7.36 0.0003 显著 残差 0.0236 14 0.0017 失拟项 0.0118 9 0.0013 0.5589 0.7889 不显著 纯误差 0.0118 5 0.0024 总和 0.2094 29
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