Advanced Search
JI Zhaohui, WANG Hongyang, SUN Zhen, DING Kunying. Effect of interfacial bonding properties of WC-17Co/Ti-6Al-4V after roughening on the face of substrate[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2018, 39(5): 77-81. DOI: 10.12073/j.hjxb.2018390127
Citation: JI Zhaohui, WANG Hongyang, SUN Zhen, DING Kunying. Effect of interfacial bonding properties of WC-17Co/Ti-6Al-4V after roughening on the face of substrate[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2018, 39(5): 77-81. DOI: 10.12073/j.hjxb.2018390127

Effect of interfacial bonding properties of WC-17Co/Ti-6Al-4V after roughening on the face of substrate

More Information
  • Received Date: November 24, 2016
  • The effect of surface roughness of Ti-6Al-4V alloy on the bonding strength of tungsten carbide (WC-17Co) coatings sprayed by high velocity oxygen fuel (HVOF) was studied. Different sand blasting processes were used to generate different surface roughness with 22.003, 20.845 and 14.765 μm for No.1, No.2 and No.3 specimens, respectively. The WC-17Co coating with a thickness of 0.3 mm was deposited on the surface of titanium alloy by HVOF technology. The three-point bending test was performed on the WC-17Co/Ti-6Al-4V sample, and the interface morphology was observed by a scanning electron microscope. It was found that cracks propagated seriously at the interface of No. 3 specimen, resulting in the coating peeling off. No. 2 sample showed the best bonding properties. The four-point bending method was used to test the fracture energy release rates between the coating and the substrate, and the values of release rate were 239.7, 259.0 and 200.1 J/m2for three specimens, respectively.
  • 中国表面工程协会热喷涂专业委员会. 中国热喷涂年鉴2003年版[M]. 北京: 科学技术文献出版社, 2004.[2] Amada S, Hirose T. Influence of grit blasting pre-treatment on the adhesion strength of plasma sprayed coatings: fractal analysis of roughness[J]. Surface and Coatings Technology, 1998, 102: 132-137.[3] Wang Y Y, Li C J, Ohmori A. Influence of substrate roughness on the bonding mechanisms of high velocity oxy-fuel sprayed coatings[J]. Thin Solid Films, 2005, 485: 141-147.[4] Staia M H, Ramos E, Carrasquero A,et al. Effect of substrate roughness induced by grit blasting uponadhesion of WC-17% Co thermal sprayedcoatings[J]. Thin Solid Films, 2000, 377: 657-664.[5] Yang Hui, Pan Shaoming. Effect of substrate roughness on bond strength of coatings[J]. Hot Working Technology, 2008, 37(15): 118-121.[6] Yang Hui, Pan Shaoming. Bonding mechanism of WC-12Co coatings prepared by supersonic plasma spraying on 45 steel[J]. Transactions of Materials and Heat Treatment, 2009, 30(3): 187-191.[7] Lia H, Khora K A, Cheangb P. Young’s modulus and fracture toughness determination of high velocity oxy-fuel-sprayed bioceramic coatings[J]. Urface and Coatings Technology, 2002, 155: 21-32.
  • 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 (558) PDF downloads (1) Cited by(36)

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return