Effect of laser cleaning on surface stress and corrosion of SMA490BW steel welded joint
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摘要: 为提高耐候钢焊接质量，将皮秒脉冲激光清洗技术引入到耐候钢焊接的清洗中，并研究了皮秒脉冲激光清洗对SMA490BW耐候钢焊接接头抗应力腐蚀性能的影响. X射线残余应力测试试验结果表明,激光清洗后试样表面残余拉应力增加130 ~ 200 MPa，不利于应力腐蚀性能的提高. 但周期浸润腐蚀试验证明,激光清洗使试件表面的耐蚀性明显提高，这有利于提高接头的抗应力腐蚀性能. 最后，通过三点弯应力腐蚀试验证明,激光清洗提高了耐候钢接头的抗应力腐蚀性能. 其原因为激光清洗使试样表面形成微米级（0.8 ~ 1.2 μm）紧密堆积的柱状颗粒，提高了试件表面的耐蚀性大于表面残余拉应力带来的影响.Abstract: In order to ensure high quality weathering steel welding, we introduce picosecond pulse laser cleaning technology into welded joint cleaning. This paper studied the effect of picosecond pulsed laser cleaning on stress corrosion resistance of SMA490BW weathering steel welded joints. X-ray residual stress test shown that: The residual stress on the surface of the specimens after laser cleaning increased 130 ~ 200 MPa, which was not conducive to the performance of stress corrosion. But, the cyclic corrosion test proved that: The corrosion resistance of the specimen surface has been improved by laser cleaning, which is helpful to the stress corrosion resistance of the joint. Finally, the three-point bending stress corrosion test proved that: Laser cleaning can improve the stress corrosion resistance of the weathering steel joint. The reason is that the laser cleaning makes the surface formed micron scale (0.8 ~ 1.2 μm) tightly packed columnar particles, which improved the corrosion resistance more than the influence of the residual tensile stress.
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Keywords:
- laser cleaning /
- residual stress /
- stress corrosion /
- surface micro-nano structure
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0. 序言
动力电池作为新能源电动汽车的核心组件，其性能和安全性依赖于制造过程中每个环节的质量控制. 激光焊因其高精度、高速度等优点，被广泛应用于方形和软包动力电池模组制造[1]，高质量的焊接连接确保电芯与模组之间的电流传输稳定，有效降低接触电阻，减少发热量，提升能量转换效率和延长电池寿命[2]. 然而，传统单模高斯激光焊易导致金属飞溅缺陷，增加电池接头的电阻和热损耗，降低整体性能和安全性[3]. 为应对这些挑战，探索新型激光焊技术，以实现飞溅的抑制成为关键方向.
近年来，可调环模VBP激光作为新型激光热源，通过合理分配激光能量，提升焊接过程的稳定性，但激光焊过程过于复杂，热−力耦合效应对成形质量的影响规律尚不明晰. 前期研究表明[4]，在内外环激光功率比为1∶2 ~ 1∶3时，焊接稳定且飞溅率最低.然而，这些研究主要停留在定性分析，缺乏准确的飞溅状态量化研究，需要开发高时空分辨的实时测量方法，为精细调控激光能量提供指导，也为焊接质量闭环控制提供关键反馈.
声发射传感方法和光电同轴传感系统在激光焊缺陷预测和熔透状态分类识别方面取得进展[5–7]，但受噪声干扰和间接传感技术的局限.而高分辨率光学相干层析成像(OCT)技术可实现焊接过程中匙孔深度的精确测量[8]，但不能直接与实际焊接质量等同，所以需要结合机器学习方法，建立匙孔深度与飞溅状态的数据驱动模型. 为此，搭建基于OCT传感技术的VBP激光焊实时监测系统，通过1DCNN-BiLSTM深度模型，实现对焊接过程飞溅状态的精准预测，最终在动力电池VBP激光焊平台试验中证实方法的有效性和可靠性.
1. 试验设计与数据采集
1.1 试验材料与设备
试验所用的材料为1060铝合金，材料尺寸为100 mm × 80 mm × 2 mm. 如图1所示，搭建了一套基于OCT传感技术的铝合金VBP激光焊实时监测平台，主要由自主设计研发的谱域光学相干层析成像(SD-OCT)模块和数据采集系统、高功率准直聚焦焊接头(KCTII BLFIW-01)和VBP激光器(EVERFOTON 长飞光坊 FFSC-2000SM/4000)等构成，其中内外环激光峰值功率分别为2 kW和4 kW，内外环芯径为14/100 μm，试验工艺参数设计见表1.
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图 1 基于光学相干断层扫描技术可调环模激光焊监测平台Figure 1. Laser welding monitoring platform based on optical coherence tomography technology表 1 试验工艺参数Table 1. Experiment parameters design内环功率
P1/W外环功率
P2/W离焦量
f/mm焊接速度
v/(mm·s−1)保护气体流量
q/(L·min−1)0 ~ 1 500 0 ~ 1 500 0 150 25 1.2 光学相干断层扫描分析
如图2所示，采用SD-OCT测量模块和数据采集系统，采样频率为250 kHz，轴向分辨率达20 μm，实现激光焊过程中的匙孔深度在线测量. 具体原理如下：利用低相干光源(SLD)通过光纤耦合器分成两个光束，一个光束经过工件平面返回，另一个光束与激光束同轴到达匙孔底部返回. 两个光束经过光纤耦合器干涉，利用光谱仪线阵相机获取光谱干涉条纹图像，对其功率谱密度进行逆傅里叶变换处理，从而获取匙孔深度信息.
1.3 “匙孔深度−飞溅状态”的数据集构建
根据前期高速摄影观察研究[4]以及图3可知，飞溅发生最小周期为12 ms左右，飞溅缺陷长度一般在2 ~ 6 mm，根据焊接速度150 mm/s换算飞溅发生的周期一般为23 ~ 40个匙孔深度数据，则取单个采集数据量为400个. 进行了30余组不同内外环激光功率试验，并对预处理后的匙孔深度数据进行分类，有飞溅标注为S(spatter)，无飞溅标注为NS(no spatter)，得到飞溅数据581个，无飞溅数据1 065个.
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图 3 匙孔深度数据波动特征与飞溅关系Figure 3. Relationship between keyhole depth data fluctuations and spatter. (a) spatter; (b) no spatter2. 飞溅评估模型设计
2.1 模型单元设计
2.1.1 1DCNN单元
焊接过程采集到的匙孔深度数据是一维时间数据，其局部时序相关性反映出了焊接质量，考虑到其数据特征，使用1DCNN网络单元也就是一维卷积层对数据进行特征提取并捕捉时序关系[9]，且卷积层后通常紧跟一层池化层， 其可对特征进行二次提取，同时减少因卷积运算而产生的参数，进而降低网络的训练代价.
2.1.2 BiLSTM单元
由于匙孔深度数据包含了丰富的焊接飞溅特征信息，但其信号时序波动关系复杂，且包含许多冗余信息. 为有效过滤噪声和冗余信息，并提取有用的深度特征，在1DCNN后使用双向长短期记忆网络(BiLSTM)，可在提取局部特征的基础上进一步捕捉全局特征和序列依赖关系，增强对飞溅的评估能力[10].
BiLSTM单元由两层相反的长短时记忆网络(long short-term memory，LSTM)组成，匙孔深度特征通过输入门it进入网络，单元状态Ct中储存了之前累积的匙孔深度特征的长期记忆，遗忘门ft对长期记忆的匙孔深度特征选择性丢弃，只保留重要的信息，输出门ot将当前输入的匙孔深度特征和上阶段积累的前期记忆相结合作为此时刻网络的输出，通过这种方式，可以极大程度地保留每个时间步中的匙孔深度特征，更准确地反映焊接过程的状态和质量.
2.2 超参数优化
贝叶斯优化通过揭示神经网络黑箱函数的全局最优解，能最大化资源利用率，处理噪声数据，并有效利用非连续空间达到全局最小[11]. TPE算法在建模阶段利用评估结果构建概率模型，分别建立x属于更好结果和较差结果时的概率密度函数. 在采样阶段，EI的计算基于两个模型的比值，通过选择使EI(x)最大的x选定效果较好的超参数. 各模型的卷积核尺寸为3，Batchsize为256，Epochs为200，损失函数为交叉熵模型. 通过此法得到各种模型的最佳超参数见表2.
表 2 各模型最佳超参数Table 2. Optimal hyperparameters for each model模型名称 各卷积核个数N/个 LSTM层数n1/层 隐藏层数n2/层 Dropout值 学习率α(10−5) 总参数数量M 1DCNN 32/256/512 — — 0.40 17 444 386 LSTM — 4 128 0.20 5.7 465 970 BiLSTM — 3 256 0.30 8.1 369 464 1DCNN-LSTM 16/256 2 128 0.37 1.0 344 482 1DCNN-BiLSTM 32/128 2 128 0.14 1.4 676 242 2.3 1DCNN-BiLSTM复合模型
1DCNN-BiLSTM模型架构如图4所示. 模型首先通过一维卷积层提取匙孔深度数据的局部特征，然后通过最大池化层下采样，减少特征长度和计算复杂度. 经过两次这样的操作后，使用双向长短期记忆网络(BiLSTM)提取数据的全局特征和序列依赖关系. 引入Adam优化器，并通过Dropout层随机丢弃部分神经元，防止过拟合，提高泛化能力，最后通过全连接层将匙孔深度的飞溅特征转换为飞溅(S)状态和无飞溅(NS)状态的概率分布.
3. 试验结果与分析
应对识别飞溅使用二分类中常用的准确率A(accuracy)精确率P(precision)、召回率R(recall)和 F1值(F)4个指标对模型性能进行评估. 各指标对比结果见表3，1DCNN-BiLSTM模型的测试集准确率和损失曲线如图5所示，可以看出1DCNN模型在精准度上优于LSTM和BiLSTM模型，表明匙孔深度数据在飞溅时会导致局部数据波动，这使得对局部敏感的卷积模型表现更好. 1DCNN-BiLSTM模型随着迭代次数增加，损失值逐渐下降且稳定，这说明1DCNN-BiLSTM模型结合了1DCNN和BiLSTM的优点，在挖掘复杂时序波动关系以及飞溅特征和应对冗余信息方面表现优异，有效实现了激光焊过程的飞溅评估.
表 3 各模型指标Table 3. Metrics for each model模型名称 准确率
A(%)精确率
P(%)召回率
R(%)F1值
F(%)1DCNN 86.93 86.95 86.93 86.94 LSTM 80.24 83.21 80.24 78.87 BiLSTM 82.06 85.77 82.06 80.67 1DCNN-LSTM 97.87 97.82 97.87 97.85 1DCNN-BiLSTM 99.69 99.68 99.69 99.65 4. 结论
(1) 针对动力电池铝合金VBP激光焊过程，设计并搭建了基于SD-OCT传感技术的激光焊过程监测平台，实时获取了动态焊接过程的匙孔深度信息，并构建了“匙孔深度−飞溅状态”的样本数据集.
(2) 构建了1DCNN-BiLSTM算法的复合模型，深度挖掘了反映飞溅动力学的局部—全部时序特征.
(3) 相比1DCNN，LSTM和BiLSTM模型，提出的1DCNN-BiLSTM复合模型能够准确地判断飞溅状态，其识别准确率达到99.69%，能够满足VBP焊接飞溅定量评价要求，并在实际焊接平台得到验证.
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图 1 X衍射线残余应力试验
Figure 1. X-ray diffraction residual stress test. (a) μ-X360n portable X-ray residual stress test equipment; (b) illustration of residual stress testing; (c) Debye ring of the electrolytic polishing surface
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图 2 周期浸润腐蚀试验
Figure 2. Periodical immersed corrosion test. (a) illustration of periodical immersed corrosion test system; (b) the specimen before corrosion; (c) the uncleaned specimen after 100 h corrosion; (d) the cleaned specimen after corrosion
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图 3 三点弯应力腐蚀试验
Figure 3. Three-point bending stress corrosion specimen design. (a) specimen interception and design on the joint; (b) illustration of three-point bending loading
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图 4 试件表面形貌SEM图
Figure 4. The SEM images of the specimen surface morphology. (a) original state; (b) electrolytic polishing; (c) single laser cleaning; (d) double laser cleaning
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图 5 不同表面处理方式接头残余应力对比
Figure 5. The comparison of surface residual stress with different surface treatment methods. (a) a view of the specimen and test line; (b) the test results of surface residual stress
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图 6 周期浸润腐蚀试样的观察与分析
Figure 6. Observation and analysis of periodical immersed corrosion tests specimens. (a) comparison of corrosion rates of each specimens; (b) surface morphology of C-1 specimen after corrosion; (c) surface morphology of C-2 specimen after corrosion; (d) surface morphology of C-5 specimen after corrosion
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图 7 三点弯应力腐蚀试验
Figure 7. Three-point bending specimens after test. (a) the view of three-point bending specimens; (b) the fracture of the sandpaper polished specimen after bluing; (c) the fracture of the laser cleaning specimen after bluing
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图 8 三点弯应力腐蚀试验结果
Figure 8. The results of three-point bending stress corrosion test. (a) the crack of group A specimen; (b) the original crack oxidized of group A fracture; (c) the detail of the group B specimen fracture; (d) the view of the group B specimen fracture
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图 9 激光清洗后试件表面微纳结构
Figure 9. Surface micro-nano structures after laser cleaning. (a) Surface topography of weathering steel after laser cleaning; (b) Surface micro-nano structure of region B; (c) Surface micro-nano structure of region C
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图 10 激光清洗试样上亚微米柱状颗粒的EDS结果
Figure 10. The EDS results of the submicron cylindrical particles on laser cleaning specimen
表 1 周期浸润试验结果
Table 1 The results of periodical immersed corrosion tests
试样编号 表面状态 试样尺寸L × W × H/mm 腐蚀前重量G1/g 腐蚀后重量G2/g 失重率RW(%) C-1 原始状态 59.96 × 40.18 × 1.92 37.10 36.41 1.86 C-2 砂纸打磨 59.92 × 39.98 × 1.90 36.22 35.44 2.15 C-3 砂纸打磨 59.96 × 40.08 × 1.92 36.77 35.96 2.20 C-4 激光清洗 60.00 × 39.92 × 1.94 36.97 36.47 1.35 C-5 激光清洗 59.92 × 40.06 × 1.92 36.66 36.02 1.75 
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[1] Yao Caizhen, Ye Yayun, Jia Baoshen, et al. Polarization and fluence effects in femtosecond laser induced micro/nano structures on stainless steel with antireflection property[J]. Applied Surface Science, 2017, 425: 1118 − 1124. doi: 10.1016/j.apsusc.2017.07.157
[2] Zhang F D, Liu H, Suebka C, et al. Corrosion behaviour of laser-cleaned AA7024 aluminium alloy[J]. Applied Surface Science, 2018, 435: 452 − 461. doi: 10.1016/j.apsusc.2017.11.141
[3] Tam A C, Park H K, Grigoropoulos C P. Laser cleaning of surface contaminants[J]. Applied Surface Science, 1998, s 127–129: 721 − 725.
[4] Buccolieri G, Nassisi V, Buccolieri A, et al. Laser cleaning of a bronze bell[J]. Applied Surface Science, 2013, 272(5): 55 − 58.
[5] 齐扬, 叶亚云, 王海军, 等. 激光清除石质文物表面污染物的作用机制[J]. 中国激光, 2015, 42(6): 0603001 − 107. doi: 10.3788/CJL201542.0603001 Qi Yang, Ye Yayun, Wang Haijun, et al. Mechanisms of laser cleaning of contamination on surface of stonework[J]. Chinese Journal of Lasers, 2015, 42(6): 0603001 − 107. doi: 10.3788/CJL201542.0603001
[6] Kearns A, Fischer C, Watkins K G, et al. Laser removal of oxides from a copper substrate using Q-switched Nd: YAG radiation at 1064nm, 532nm and 266nm[J]. Applied Surface Science, 1998, s 127–129: 773 − 780.
[7] Alshaer A W, Li L, Mistry A. The effects of short pulse laser surface cleaning on porosity formation and reduction in laser welding of aluminium alloy for automotive component manufacture[J]. Optics & Laser Technology, 2014, 64(4): 162 − 171.
[8] 于影霞, 丁江灏, 谢学涛, 等. 转向架用SMA490BW钢焊接接头超高周疲劳性能的影响因素[J]. 中国铁道科学, 2018, 39(1): 100 − 107. doi: 10.3969/j.issn.1001-4632.2018.01.14 Yu Yingxia, Ding Jianghao, Xie Xuetao, et al. Influence factors for very high cycle fatigue performance of SMA490BW steel welded joint for bogie[J]. China Railway Science, 2018, 39(1): 100 − 107. doi: 10.3969/j.issn.1001-4632.2018.01.14
[9] 何柏林, 邓海鹏, 魏康. 超声冲击对SMA490BW钢焊接接头超高周疲劳性能的影响[J]. 中国表面工程, 2017, 30(4): 64 − 70. doi: 10.11933/j.issn.1007-9289.20170307002 He Bolin, Deng Haipeng, Wang Kang. Influence of UIT on VHCF Performance of SMA490BW steel welded joints[J]. China Surface Engineering, 2017, 30(4): 64 − 70. doi: 10.11933/j.issn.1007-9289.20170307002
[10] Psyllaki P, Oltra R. Preliminary study on the laser cleaning of stainless steels after high temperature oxidation[J]. Materials Science & Engineering A, 2009, 282(1): 145 − 152.
[11] Eklund B, Johansson L, Ytreberg E. Contamination of a boatyard for maintenance of pleasure boats[J]. Journal of Soils & Sediments, 2014, 14(5): 955 − 967.
[12] 佟艳群, 张昂, 黄建宇, 等. 焊前激光预处理的铝合金表面氧含量的分布及影响[J]. 中国激光, 2019, 46(2): 56 − 64. Tong Yanqun, Zhang Ang, Huang Jianyu, et al. Distribution and influence of oxygen content of laser pretreated aluminum alloy surface before welding[J]. Chinese Journal of Lasers, 2019, 46(2): 56 − 64.
[13] 刘洪伟, 周毅鸣. 船用板材激光除锈应用技术[J]. 造船技术, 2016(6): 87 − 93. doi: 10.3969/j.issn.1000-3878.2016.06.016 Liu Hongwei, Zhou Yiming. Technology and application of optical fiber laser derusting of ship steel plates[J]. Marine Technology, 2016(6): 87 − 93. doi: 10.3969/j.issn.1000-3878.2016.06.016
[14] Li Huating, Zhou Jianzhong, Sun Qi, et al. Electrochemical corrosion properties of AH32 steel via laser cleaning[J]. Laser & Optoelectronics Progress, 2019, 56(21): 123 − 128.
[15] Vagapov I K, Ganiyev M M, Shinkaryov A S. Studies of ultrasonic impact machining influence on the value and distribution of residual stresses in welded blanks[J]. Russian Aeronautics, 2005, 48(2): 85 − 90.
[16] Daavari M, Vanini S A S. Corrosion fatigue enhancement of welded steel pipes by ultrasonic impact treatment[J]. Materials Letters, 2015, 139: 462 − 466. doi: 10.1016/j.matlet.2014.10.141
[17] Lin J, Ma N, Lei Y, et al. Measurement of residual stress in arc welded lap joints by cosα, X-ray diffraction method[J]. Journal of Materials Processing Technology, 2016, 243: 387 − 394.
[18] Yang G, Wang B, Tawfiq K, et al. Electropolishing of surfaces: theory and applications[J]. Surface Engineering, 2017, 33(2): 1 − 18.
[19] Weng L, Wu H Y, Du L X. Corrosion behavior of high-Ti weathering resistant steel in simulated marine climate[J]. Transactions of Materials and Heat Treatment, 2018, 39(5): 94 − 99.
[20] Park J U, Lee H W. Effects of initial condition of steel plate on welding deformation and residual stress due to welding[J]. Journal of Mechanical Science & Technology, 2007, 21(3): 426 − 435.
[21] Chen Jianjun. Design and development of high-performance weathering steel corrosion test cycle infiltration system[D]. Nanjing: Northeastern University, 2011.
[22] Li Zerong, Li Bin. Influences of different steel materials on the formation rates of bluing coatings[J]. Plating & Finishing, 2017, 39(2): 39 − 42.
[23] Koch J, Korte F, Bauer T, et al. Nanotexturing of gold films by femtosecond laser-induced melt dynamics[J]. Applied Physics A, 2005, 81(2): 325 − 328. doi: 10.1007/s00339-005-3212-6
[24] Masato Y, Hiroo N, Toshihei M, et al. Structure of protective rust layers formed on weathering steels by long-term exposure in the industrial atmospheres of Japan and North Americaf[J]. ISIJ International, 1998, 38(3): 285 − 290. doi: 10.2355/isijinternational.38.285
[25] 张侠洲, 高立军, 姚仲成, 等. 合金元素对耐候熔敷金属力学及耐蚀性能的影响[J]. 焊接学报, 2019, 40(5): 154 − 160. doi: 10.12073/j.hjxb.2019400144 Zhang Xiazhou, Gao Lijun, Yao Zhongcheng, et al. Effect of alloy element on mechanical and corrosion resistance properties of weathering steel deposited metal[J]. Transactions of the China Welding Institution, 2019, 40(5): 154 − 160. doi: 10.12073/j.hjxb.2019400144
[26] Horton D. Foreword: Carbohydrate research: a half century of carbohydrate science[J]. Carbohydrate Research, 2015, 403: 3 − 7. doi: 10.1016/j.carres.2014.06.016
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