Advanced Search
MA Yanyi, WANG Haiyan, ZHANG Yupeng, YI Yaoyong, DONG Fuyu. Crystallization control and microstructural properties of laser welded Zr67.8Cu24.7Al3.43Ni4.07 bulk metallic glasses[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2019, 40(12): 138-142. DOI: 10.12073/j.hjxb.2019400327
Citation: MA Yanyi, WANG Haiyan, ZHANG Yupeng, YI Yaoyong, DONG Fuyu. Crystallization control and microstructural properties of laser welded Zr67.8Cu24.7Al3.43Ni4.07 bulk metallic glasses[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2019, 40(12): 138-142. DOI: 10.12073/j.hjxb.2019400327

Crystallization control and microstructural properties of laser welded Zr67.8Cu24.7Al3.43Ni4.07 bulk metallic glasses

More Information
  • Received Date: April 29, 2019
  • Laser welding was employed to weld Zr67.8Cu24.7Al3.43Ni4.07 bulk metallic glasses (BMGs). The effects of laser power and welding speed on the microstructures of different regions in the joints were studied. The crystallization control law of laser welded BMG joints is expounded, and the relationship between microstructure characteristics and hardness of as-welded joints is discussed. The results showed that the laser welding technology with high welding speed and high energy density is beneficial to maintain the amorphous structure of the molten zone in Zr67.8Cu24.7Al3.43Ni4.07 BMG joints, accompanied by some nano-grains forming. The crystallization happening in heat affected zones is severe. Laser power has a great influence on the complete penetration of as-welded joints. The degree of crystallization in heat affected zones can be effectively controlled through lowering laser power or increasing the welding speed to reduce heat input. Vickers hardness tests reveal that the hardness of the molten zones of the welded joint is slightly higher than that of base material, and the hardness of heat affected zones is significantly lower than that of the base material.
  • 王彦芳, 司爽爽, 宋增金, 等. 电火花沉积非晶涂层的组织结构与摩擦磨损性能[J]. 焊接学报, 2018, 39(7):121-124
    Wang Yanfang, Si Shuangshuang, Song Zengjin, et al. Microstructure and tribology behaviors of Zr-based amorphous coating on ZL101 by electro-spark deposition[J]. Transactions of the China Welding Institution, 2018, 39(7):121-124
    Qiao J, Jia H, Liaw P K. Metallic glass matrix composites[J]. Materials Science and Engineering R Reports, 2016, 100:1-69.
    王 廷, 石志远, 李 宁, 等. Cu46Zr46Al8非晶合金电子束焊接特性分析[J]. 焊接学报, 2018, 39(8):38-41
    Wang Ting, Shi Zhiyuan, Li Ning, et al. Characteristic of electron beam welded Cu46Zr46Al8 BMGs[J]. Transactions of the China Welding Institution, 2018, 39(8):38-41
    文 驰. Zr基非晶合金扩散焊技术研究[D]. 武汉:华中科技大学, 2015.
    张英明, 袁子洲, 孙 慧, 等. 非晶合金连接的研究现状[J]. 材料学报, 2010, 24(9):89-92
    Zhang Yingming, Yuan Zizhou, Sun Hui, et al. Research status of the bonding for amorphous alloys[J]. Materials Reports, 2010, 24(9):89-92
    Williams E, Lavery N. Laser processing of bulk metallic glass:a review[J]. Journal of Materials Processing Technology, 2017, 247:73-91.
    Wirginia P. Structure and properties of Zr-Based bulk metallic glasses in as-cast state and after laser welding[J]. Materials, 2018, 11(7):1-14.
    Wang H S, Chen H G, Jang J S C, et al. Combination of a Nd:YAG laser and a liquid cooling device to (Zr53Cu30Ni9Al8)Si0.5 bulk metallic glass welding[J]. Materials Science & Engineering A, 2010, 528(1):338-341.
    Chen B, Shi T, Li M, et al. Crystallization of Zr55Cu30Al10Ni5 bulk metallic glass in laser welding:simulation and experiment[J]. Advanced Engineering Materials, 2015, 17(4):483-490.
    Wang G, Huang Y J, Shagiev M, et al. Laser welding of Ti40Zr25Ni3Cu12Be20 bulk metallic glass[J]. Materials Science & Engineering A, 2012, 541(9):33-37.
    Wang H S, Chen H G, Jang S C. Microstructure evolution in Nd:YAG laser-welded (Zr53Cu30Ni9Al8)Si0.5 bulk metallic glass alloy[J]. Journal of Alloys & Compounds, 2010, 495(1):224-228.
    Chen H L, Li L J, Lin T L. Formation of segregation morphology in crystalline/amorphous polymer blends:molecular weight effect[J]. Macromolecules, 1998, 31(7):2255-2264.
    胡 鹏. 锆基非晶合金及复合材料凝固行为的研究[D]. 兰州:兰州理工大学, 2018.
  • Related Articles

    [1]XIE Yuxin, GONG Yefei, GU Xinhao, CHEN Xiaobin, WANG Meng, XU Huigang. Research on weld surface defect detection method based on RGB-D feature fusion[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(12): 72-78. DOI: 10.12073/j.hjxb.20230712002
    [2]GUO Zhongfeng, LIU Junchi, YANG Junlin. Weld recognition based on key point detection method[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(1): 88-93. DOI: 10.12073/j.hjxb.20230204001
    [3]WANG Rui, HU Yunlei, LIU Weipeng, LI Haitao. Defect detection of weld X-ray image based on edge AI[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2022, 43(1): 79-84. DOI: 10.12073/j.hjxb.20210516001
    [4]LI Hexi, HAN Xinle, FANG Zaojun. A visual model of welding robot based on CNN deep learning[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2019, 40(2): 154-160. DOI: 10.12073/j.hjxb.2019400060
    [5]GAO Weixin, HU Yuheng, WU Xiaomeng. A new algorithm for detecting defects of sub-arc welding x-ray image based on compress sensor theory[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2015, 36(11): 85-88.
    [6]LI Xueqin, LIU Peiyong, YIN Guofu, JIANG Honghai. Weld defect detection by X-ray images method based on Fourier fitting surface[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2014, 35(10): 61-64.
    [7]SHAO Jiaxin, DU Dong, ZHU Xinjie, GAO Zhiling, WANG Chen. Weld defect detection of double sides weld based on X-ray digitized image[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2010, (11): 21-24.
    [8]CHEN Ming, MA Yuezhou, CHEN Guang. Weld defects detection for X-ray linear array real-time imaging[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2007, (6): 81-84.
    [9]CAI Guorui, DU Dong, TIAN Yuan, HOU Runshi, GAO Zhiling. Defect detection of X-ray images of weld using optimized heuristic search based on image information fusion[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2007, (2): 29-32.
    [10]LIANG De-qun, SHEN San, YANG Hai-jun. Measurement of Deep Sclae on Weld Defects based on Point X-Pay Source[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2000, (3): 5-8.
  • Cited by

    Periodical cited type(19)

    1. 戴铮,刘骁佳,潘泉. 基于CCBFE-RCNN模型的焊缝X射线图像缺陷识别算法. 焊接学报. 2025(01): 24-33 . 本站查看
    2. 简珂,王帅,李强. 基于连通域分析的钢管焊缝缺陷检测方法. 测控技术. 2025(02): 11-17 .
    3. 李闯,马行,穆春阳,刘永鹿,秦政硕,张弘. 改进YOLOv3的轻量级铸件焊缝表面缺陷检测. 组合机床与自动化加工技术. 2024(01): 156-159+163 .
    4. 张婷,王登武. 基于空洞分层注意力胶囊网络的X射线焊缝缺陷识别方法. 宇航计测技术. 2024(02): 45-51 .
    5. 穆春阳,李闯,马行,刘永鹿,杨科,刘宝成. 改进YOLOv7-tiny的轻量化大型铸件焊缝缺陷检测. 组合机床与自动化加工技术. 2024(07): 156-160 .
    6. 傅留虎. 基于轻量化特征增强网络的焊缝缺陷检测方法. 焊接. 2024(07): 38-49+57 .
    7. 李国栋,吴志生,彭甫镕,郝康将,昝晓亮,郭威. 基于有监督对比学习的焊缝缺陷X射线检测方法. 焊接. 2024(07): 7-14 .
    8. 苏志威,黄子涵,邱发生,郭朝阳,殷晓芳,邬冠华. 基于改进YOLOv8的航空铝合金焊缝缺陷检测方法. 航空动力学报. 2024(06): 121-129 .
    9. 宋杰三. 基于改进Yolov8的焊缝缺陷检测研究. 中国设备工程. 2024(14): 196-199 .
    10. 李兴红. 基于深度学习的液氯罐车射线图像缺陷自动识别研究. 中国高新科技. 2024(12): 15-17 .
    11. 张小刚,俞东宝,汤慧,朱永利. 基于深度学习的X射线燃料棒端塞缺陷自动检测方法研究. 原子能科学技术. 2024(08): 1767-1776 .
    12. 贾韶辉,李亚平,高炜欣,彭云超,张新建,王玉霞. 基于X射线图像与稀疏描述的管道环焊缝缺陷自动识别法. 油气储运. 2024(09): 1048-1055+1079 .
    13. 张睿,李吉. 多级多尺神经网络自搜索的焊缝缺陷语义分割. 计算机辅助设计与图形学学报. 2024(11): 1750-1760 .
    14. 谢雨欣,龚烨飞,谷心浩,陈晓彬,王萌,徐惠钢. 基于RGB-D特征融合的焊缝表面缺陷检测方法. 焊接学报. 2024(12): 72-78 . 本站查看
    15. 张睿,高美蓉,傅留虎,张鹏云,白晓露,赵娜. 基于多域多尺度深度特征自适应融合的焊缝缺陷检测研究. 振动与冲击. 2023(17): 294-305+313 .
    16. 郑孝干,杨毅豪,林啸,吕雷,肖毓勇,黄潞璐. 基于AI的输电线路导线断散股缺陷检测方法. 电工技术. 2023(20): 69-71+74 .
    17. 田伟倩,朱华兵,张淋,胡斌. 基于卷积神经网络的工业缺陷检测研究进展. 中国特种设备安全. 2023(12): 1-7 .
    18. 邓智超,颜润明,杨蕙同,陈浩林,赖锦祥,雷亮. 基于改进残差网络的多视图焊点缺陷检测. 焊接学报. 2022(03): 56-62+116 . 本站查看
    19. 杨国威,张金丽. 基于光栅投影的焊后焊缝表面三维测量. 焊接学报. 2022(04): 100-105+112+119-120 . 本站查看

    Other cited types(17)

Catalog

    Article views (311) PDF downloads (41) Cited by(36)

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return