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采用普通灰铸铁(55 mm×55 mm×30 mm)的规整块状材料作为激光熔覆基体,将熔覆基体材料待加工区域及周边污渍进行清洗处理,然后采用超声波探伤技术进行检测,确认基体材料待加工区及周边无气孔、裂纹等表面缺陷后,将316L合金粉末和基体材料放置22°±1°的数字化测试实验室内静置24 h后进行激光熔覆增材实验。经检测常温状态下HT250灰铸铁硬度为30 HRC,316L不锈钢粉末的显微硬度为180 HV,两者的主要化学成分如表 1所示[14]。
表 1 316L不锈钢粉末和HT250基体材料成分
Table 1. Composition of 316L stainless steel powder and HT250 matrix material
mass fraction/% C Si Ni P S Mn Cr Mo O Fe 316L 0.018 0.92 11.3 — — — 15 2.5 0.33 balance HT250 3.15 1.78 — 0.08 0.12 0.78 — — — balance -
采用遗传算法的多目标工艺优化方法,对熔覆工艺参数(激光功率、扫描速率、送粉量等)开展多因素耦合分析,通过实验明确上述工艺参数的取值范围及相关优化目标[15],具体如下:基于工作效能考量,其熔覆效率应大;为减少熔覆元素的扩展影响[16],其热影响区范围Ai应小;针对表面配合性能及外观要求,其表面粗糙度Rz需小;依据成分扩散原则及结合实际制备经验,稀释率约束条件的设定原则上应控制在5%~25%为佳,其学习模型如下式所示:
$ F(X)=\left\{\begin{array}{l} \max \left[v_i \times S_i\left(P_i, v_i, f_i, D_i, L_i, J_i\right)\right] \\ \min \left[A_i\left(P_i, V_i, f_i, D_i, L_i, J_i\right)\right] \\ \min \left[R_{\mathrm{z}}\left(P_i, V_i, f_i, D_i, L_i, J_i\right)\right] \end{array}, \right. $
(1) 式中:F(X)为优化目标; vi为熔覆速率;Si为熔覆面积;Pi为熔覆功率;fi为送气量;Di为光斑直径;Li为离焦量;Ji为转盘速度;ηi为稀释率;下标i为实验次数。根据回归分析模型生成相应的候选解,基于优化实验明确交叉概率、变异概率等学习模型参数,将学习模型参数统一设为评价函数,以其作为适应度函数表示,获得模型优化分析的最优解集,根据实验测试结果最终获得极优解值[17-19]。相关学习算法分析如图 1所示。
如图 2实验验证平台所示,在基于遗传算法的工艺参数分析优化下,以最优解集激光工艺参数组号R7为核心基准,设置激光工艺参数进行延伸扩展实验。采用博实工业机器人与3 kW激光修复系统配备,送粉装置则采用同步装盘式PFTD-ID03设备。实验过程中将光斑尺寸控制在6 mm×3 mm范围内、送粉气压(N2)为0.35 MPa、负载气流量为495 L/h、安全气压(N2)设置为0.25 MPa[16]。
表 2为遗传算法的参数设置。种群数量设置为20,最大迭代次数设置为100,设置变异率为0.15,交叉概率设置为0.55,激光功率约束范围为2 kW~3 kW,送粉速率为0.2 g/~0.8 g/s,扫描速率为8 mm/s~10 mm/s。
表 2 基于遗传算法优化下的工艺参数表
Table 2. Process parameter table optimization based on GA
test group number laser power/W scanning speed/(mm·s-1) powder feeding rate/(g·s-1) 1 2000 8 0.25 2 2000 9 0.5 3 2000 10 0.75 4 2400 10 0.5 5 2400 9 0.25 6 2400 8 0.75 7 2800 10 0.25 8 2800 8 0.5 9 2800 9 0.75 -
结合表 2开展实验,在设定的经验参数范围内,研究HT250灰铸铁表面熔覆制备316L不锈钢合金试样,借助数字化检测仪器对试样的各项性能进行测量分析。深入开展实验,全面分析工艺参数对熔覆层各项性能的影响规律,综合遴选出最佳工艺参数。将优选激光熔覆洛氏硬度最高的试样,采用线切割加工技术对试样进行切割处理,得到最为均匀的中段试样件;同时采用数显游标卡尺将熔覆层几何尺寸中的高度和宽度进行检测分析;各熔覆试样检测过程所得的所有测量数据,均为3次以上检测所得的平均值[18-20]。
图 3为遗传算法迭代更新适应度函数的平均值和最优值。由图 3可知,随着迭代次数的增多,适应度函数曲线逐渐下降,在50代时,适应度函数平均值和最优值拟合度较高,此阶段种群稳定性强。综上可得,基于GA优化出的最佳参数为:送粉速率0.25 g/s、扫描速率10 mm/s、激光功率2.8 kW。该工艺参数环境下激光熔覆性能和效率最优。
基于GA的灰铸铁表面激光熔覆316 L工艺参数优化
Optimization of process parameters for laser cladding 316 L on gray iron surface based on GA
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摘要: 为了提升HT250灰铸铁材料的综合性能,采用遗传算法对多目标工艺参数进行优化,得出相应经验参数组,在灰铸铁材料表面进行激光熔覆316L合金实验;采用数字化检测仪器,根据试样几何形状、宏观形貌、硬度等变化规律,得出最优工艺参数组。结果表明,当设置送粉速率为0.25 g/s、扫描速率为10 mm/s、激光功率为2800 W时,316L熔覆层表面几何形状最佳,宏观形貌好,洛氏硬度最大值达37.6 HRC,试样熔覆性能良好。这一结果综合提高了灰铸铁的各项性能,为灰铸铁产品磨损后的修复再利用提供实践参考。Abstract: In order to improve the comprehensive performance of HT250 gray cast iron material, a genetic algorithm(GA) multi-objective process parameter optimization method was used to obtain the corresponding empirical parameter group. In the laser cladding 316L alloy experiment on the surface of gray cast iron material, a digital detection instrument was used to comprehensively analyze the changes in macroscopic morphology, Rockwell hardness, geometric shape, and other characteristics of the sample, and the optimal process parameter combination was analyzed and optimized. The results show that when the process parameters are respectively set to powder feeding speed of 0.25 g/s, scanning speed of 10 mm/s, and laser power of 2800 W, the surface geometry of the 316L cladding layer is the best, the macroscopic morphology is good, and the maximum Rockwell hardness reaches 37.6 HRC. The sample cladding performance is good. The comprehensive improvement of various properties of gray cast iron provides practical reference for the repair and reuse of worn gray cast iron products.
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Key words:
- laser technique /
- 316L alloy powder /
- genetic algorithm /
- macro morphology /
- multi-objective optimization /
- geometry
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表 1 316L不锈钢粉末和HT250基体材料成分
Table 1. Composition of 316L stainless steel powder and HT250 matrix material
mass fraction/% C Si Ni P S Mn Cr Mo O Fe 316L 0.018 0.92 11.3 — — — 15 2.5 0.33 balance HT250 3.15 1.78 — 0.08 0.12 0.78 — — — balance 表 2 基于遗传算法优化下的工艺参数表
Table 2. Process parameter table optimization based on GA
test group number laser power/W scanning speed/(mm·s-1) powder feeding rate/(g·s-1) 1 2000 8 0.25 2 2000 9 0.5 3 2000 10 0.75 4 2400 10 0.5 5 2400 9 0.25 6 2400 8 0.75 7 2800 10 0.25 8 2800 8 0.5 9 2800 9 0.75 -
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