Process parameters analysis of coaxial powder feeding argon arc cladding based on RSM and NSGA-II algorithm
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摘要:
为提高再修复工件表面熔覆层的质量与性能，采用自主研发的同轴送粉钨极氩弧熔覆系统，在316L不锈钢表面进行了纳米SiC增强耐磨涂层的氩弧熔覆试验，针对多因素交互影响作用下，工艺参数难以寻优的问题，提出了一种RSM与NSGA-II算法集成的工艺参数多目标优化方法.该方法以单位热输入量、送粉量和SiC质量分数为输入因素，显微硬度、熔深和稀释率为响应指标，通过响应曲面法建立熔覆层质量与工艺参数之间的回归模型，探究工艺参数对熔覆层质量的交互影响，使用NSGA-II算法寻得最优工艺参数组合，对最优工艺参数条件下制备的试样进行熔覆层质量对比与组织形貌观察.结果表明，最优参数组合为单位热输入量17.82 W/(mm·min−1)，送粉量8 g/mm，SiC质量分数2%，该条件下的试样显微硬度增大了9.9%，熔深降低了31.9%，稀释率降低了22.4%，熔覆层形貌良好无缺陷，顶部区域为均匀细小的等轴状晶粒.
Abstract:In order to improve the quality and performance of the cladding layer on the surface of the re-repaired workpiece, the argon arc cladding experiment of Nano-SiC reinforced wear-resistant coating was carried out on the surface of 316 L stainless steel by using the self-developed coaxial powder feeding tungsten argon arc cladding system. Aiming at the problem that the process parameters are difficult to optimize under the interaction of multiple factors, a multi-objective optimization method of process parameters integrated with RSM and NSGA-II algorithm is proposed. In this method, the welding unit heat input, powder feeding amount and SiC quality fraction were taken as input factors, and the microhardness, penetration depth and dilution rate were taken as response indexes. The regression model between the quality of cladding layer and process parameters was established by response surface method, and the interaction effect of process parameters on the quality of cladding layer was explored. Then, the NSGA-II algorithm was used to find the optimal combination of process parameters. Finally, the quality comparison and microstructure observation of cladding layer were carried out on the samples prepared under the optimal process parameters. The results show that the optimal parameter combination is unit heat input is 17.82 W/(mm·min−1), powder feeding amount 8g/mm and SiC quality fraction 2%. The microhardness of the specimens under these conditions increased by 9.9%, the depth of fusion decreased by 31.9%, the dilution rate decreased by 22.4%. The morphology of the cladding layer is good without defects, and the top area is uniform and fine equiaxed grains.
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图 3 熔覆层截面与测量区域示意图
Figure 3. Schematic diagram of cladding layer section and measurement area
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图 4 试样宏观形貌与截面形貌
Figure 4. Macroscopic morphology and cross-sectional morphology of the sample
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图 5 残差概率分布
Figure 5. Residual probability distribution diagram. (a) microhardness; (b) penetration depth; (c) dilution ratio
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图 7 显微硬度的响应曲面图
Figure 7. Response surface diagram of microhardness. (a) response surface (E,m,f); (b) response surface (w,m,f); (c) response surface (w,E,f)
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图 9 熔深的响应曲面图
Figure 9. Response surface diagram of penetration depth. (a) response surface (E,m,d); (b) response surface (w,m,d); (c) response surface (w,E,d)
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图 11 稀释率的响应曲面图
Figure 11. Response surface diagram of dilution ratio. (a) response surface (E,m,η); (b) response surface (w,m,η); (c) response surface (w,E,η)
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图 15 最优参数下熔覆层截面显微组织图
Figure 15. Microstructure of the cross-section of the cladding layer under the optimum parameters. (a) microstructure of the cross-section of the cladding layer; (b) partial enlargement of the lower part of Fig.15(a); (c) partial enlargement of the middle part of Fig.15(a); (d) partial enlargement of the upper part of Fig.15(a)
表 1 316L不锈钢化学成分(质量分数，%)
Table 1 Chemical composition of 316L stainless steel
C Si Mn S P Cr Ni Mo Fe ≤0.03 ≤1.00 ≤2.00 ≤0.03 ≤0.045 16.0 ~ 18.0 10.00 ~ 14.00 2.00 ~ 3.00 余量 
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表 2 工艺参数水平编码表
Table 2 Process parameter level coding table
水平 单位热输入量
E/(W·mm−1·min−1)送粉量
m/(g·mm−1)SiC含量
wSiC(质量分数，%)−1 15 4 1 0 18 6 1.5 1 21 8 2
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表 3 试验方案及对应的响应值
Table 3 Test scheme and the corresponding response value
序号 单位热输入量
E/(W·mm−1·min−1)送粉量
m/(g·mm−1)SiC含量
wSiC(质量分数，%)显微硬度
H/HV熔深
d/mm稀释率
η(%)S1 21 4 1.5 234 2.63 77.47 S2 18 4 1 258 1.80 66.35 S3 15 8 1.5 246 1.40 78.35 S4 15 6 2 265 2.34 75.94 S5 18 6 1.5 249 2.80 80.40 S6 21 6 2 236 3.20 65.10 S7 15 6 1 225 2.70 79.83 S8 18 8 2 269 1.98 69.73 S9 15 4 1.5 232 1.95 63.62 S10 18 6 1.5 253 2.5 83.77 S11 18 4 2 235 1.72 74.56 S12 21 6 1 252 3.07 84.76 S13 18 6 1.5 247 2.71 81.24 S14 18 8 1 226 1.36 71.91 S15 21 8 1.5 237 3.25 70.05 S16 18 6 1.5 248 2.65 85.23
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表 4 响应指标的回归模型方差分析
Table 4 Regression model analysis of variance of response indicators
来源 平方和 均方差 F值 P值 显微硬度 熔深 稀释率 显微硬度 熔深 稀释率 显微硬度 熔深 稀释率 显微硬度 熔深 稀释率 模型 2935.94 5.3100 776.10 326.22 0.5903 86.23 59.31 10.74 4.39 < 0.0001 0.0046 0.0428 m 84.50 0.0053 3.10 84.50 0.0053 3.10 15.36 0.10 0.16 0.0078 0.7666 0.7048 E 10.13 1.7700 13.81 10.13 1.7700 13.81 1.84 32.14 0.70 0.2237 0.0013 0.4338 w 325.13 0.0056 14.08 325.13 0.0056 14.08 59.11 0.10 0.72 0.0003 0.7601 0.4296 E·m 30.25 0.3422 123.19 30.25 0.3422 123.19 5.50 6.22 6.27 0.0574 0.0468 0.0462 m·w 1332.25 0.0864 44.78 1332.25 0.0864 44.78 242.23 1.57 2.28 < 0.0001 0.2565 0.1818 E·w 784.00 0.0610 167.27 784.00 0.0610 167.27 142.55 1.11 8.52 < 0.0001 0.3327 0.0267 m2 60.06 2.2300 369.97 60.06 2.2300 369.97 10.92 40.58 18.84 0.0163 0.0007 0.0049 E2 264.06 0.6072 1.55 264.06 0.6072 1.55 48.01 11.04 0.08 0.0004 0.0159 0.7881 w2 45.56 0.2068 38.33 45.56 0.2068 38.33 8.28 3.76 1.95 0.0281 0.1005 0.2119 残差 33.00 0.3299 117.84 5.50 0.0550 19.64 — — — — — — 失拟项 12.25 0.2823 104.01 4.08 0.0941 34.67 0.59 5.94 7.52 0.6621 0.0888 0.6580 纯误差 2.75 0.0476 13.82 0.91 0.0159 4.61 — — — — — — 总离差 2950.94 5.64 893.94 — — — — — — — — —
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