高级检索

大功率激光焊背面焊缝宽度神经网络预测

陈子琴,高向东,王煜,游德勇

陈子琴,高向东,王煜,游德勇. 大功率激光焊背面焊缝宽度神经网络预测[J]. 焊接学报, 2018, 39(11): 48-52. DOI: 10.12073/j.hjxb.2018390271
引用本文: 陈子琴,高向东,王煜,游德勇. 大功率激光焊背面焊缝宽度神经网络预测[J]. 焊接学报, 2018, 39(11): 48-52. DOI: 10.12073/j.hjxb.2018390271
CHEN Ziqin, GAO Xiangdong, WANG Yu, YOU Deyong. Weldment back of weld width prediction based on neural network during high-power laser welding[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2018, 39(11): 48-52. DOI: 10.12073/j.hjxb.2018390271
Citation: CHEN Ziqin, GAO Xiangdong, WANG Yu, YOU Deyong. Weldment back of weld width prediction based on neural network during high-power laser welding[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2018, 39(11): 48-52. DOI: 10.12073/j.hjxb.2018390271

大功率激光焊背面焊缝宽度神经网络预测

Weldment back of weld width prediction based on neural network during high-power laser welding

  • 摘要: 针对焊接过程中熔透及焊缝背面成形难以直接检测的问题,通过焊件正面和侧面的传感特征信息,对焊件背面的焊缝宽度进行预测. 用视觉传感器获取激光焊接过程中包含焊接特征信息的图像,对图像进行分割分层、模式识别和空域图像处理,准确提取焊接特征信息,发现焊接特征信息随着焊接路径的变化有着相应的变化趋势. 建立包含两个隐含层的贝叶斯神经网络,用提取到的9组特征信息作为输入,对焊件背面焊缝宽度进行预测. 通过10组焊件背面焊缝宽度的预测值与实际值的比较,验证了贝叶斯神经网络具有良好的预测能力,在焊缝不理想的状态下,也具有较好的预测能力.
    Abstract: In high-power laser welding process, it is hard to detect weld penetration conditions and back of weld shape directly. The width of back of weld was predicted by the sensing characteristics information of weld face and side surface. Visual sensors are used to capture images which contain weld characteristics information in laser welding process. Weld characteristics are extracted accurately through image segmentation, image hierarchical, pattern recognition and space image process. The extracted characteristics variation trends are corresponding to weld route change obviously. Bayes neural network that contains two hidden layers is established for back of weld width prediction of weldment, and the characteristics extracted from images are used as inputs. The compare results between prediction value and real value verified that the established Bayes neural network has good predictive ability, and better predictive stability even the weld is not ideal.
  • [1] Yang L, Ume I C. Measurement of weld penetration depths in thin structures using transmission coefficients of laser-generated Lamb waves and neural network[J]. Ultrasonics, 2017, 78: 96 ? 109.
    [2] Ola O T, Doern F E. Factors controlling keyhole-induced porosity in cold wire laser welded aluminum[J]. Journal of Laser Applications, 2017, 29(1): 012008-1 ? 8.
    [3] 赵 琳, 塚本进, 荒金吾郎, 等. 10 kW光纤激光焊接缺陷的形成[J]. 焊接学报, 2015, 36(7): 55 ? 58
    Zhao Lin, Tsukamoto S, Arakane G, et al. Formation of defects in 10 kW fiber laser welding[J]. Transactions of the China Welding Institution, 2015, 36(7): 55 ? 58
    [4] Li S, Chen G, Zhou C. Effects of welding parameters on weld geometry during high-power laser welding of thick plate[J]. International Journal of Advanced Manufacturing Technology, 2015, 79(1–4): 177 ? 182.
    [5] Li S, Chen G, Katayama S, et al. Relationship between spatter formation and dynamic molten pool during high-power deep-penetration laser welding[J]. Applied Surface Science, 2014, 303(6): 481 ? 488.
    [6] Zhang Y, Gao X. Analysis of characteristics of molten pool using cast shadow during high-power disk laser welding[J]. International Journal of Advanced Manufacturing Technology, 2014, 70(9–12): 1979 ? 1988.
    [7] Pang S Y, Chena X, Zhou J X, et al. 3D transient multiphase model for keyhole, vapor plume, and weld pool dynamics in laser welding including the ambient pressure effect[J]. Optics and Lasers in Engineering, 2015, 74: 47 ? 58.
    [8] Zou J L, Wu S K, Yang W X, et al. A novel method for observing the micro-morphology of keyhole wall during high-power fiber laser welding[J]. Materials & Design, 2016, 89: 785 ? 790.
    [9] 高向东, 张 勇, 游德勇, 等. 大功率光纤激光焊熔池形态及焊接稳定性分析[J]. 焊接学报, 2011, 32(9): 13 ? 16
    Gao Xiangdong, Zhang Yong, You Deyong, et al. Analysis of molten pool configuration and welding stability during high-power fiber laser welding[J]. Transactions of the China Welding Institution, 2011, 32(9): 13 ? 16
    [10] Pan Q, Mizutani M, Kawahito Y, et al. High power disk laser-metal active gas arc hybrid welding of thick high tensile strength steel plates[J]. Journal of Laser Applications, 2016, 28(1): 012004.
  • 期刊类型引用(11)

    1. 庞嘉尧,程伟. 铝合金搅拌摩擦焊接头疲劳性能研究进展. 兵器材料科学与工程. 2025(01): 164-175 . 百度学术
    2. 金玉花,邢逸初,周子正,吴博. 喷丸改性对7050铝合金FSW接头性能的影响. 材料导报. 2023(10): 169-173 . 百度学术
    3. 王龙权,周海涛. 7xxx高强铝合金搅拌摩擦焊研究进展. 焊接. 2023(10): 47-54 . 百度学术
    4. 周韶泽,郭硕,陈秉智,张军,兆文忠. 焊接结构超高周疲劳主S-N曲线拟合及寿命预测方法. 焊接学报. 2022(05): 76-82+118 . 本站查看
    5. 米鹏,王瑞杰,杨庆鹤. 5083铝合金带吻接FSW接头疲劳强度分析. 机械科学与技术. 2021(03): 463-469 . 百度学术
    6. 张龙,陈东高,张迎迎,戴宇,马良超,王大锋,郭庆虎,何逸凡. 7B52-T6叠层铝合金焊接接头组织及疲劳损伤行为. 兵器材料科学与工程. 2021(02): 126-130 . 百度学术
    7. 王池权,石亮,张祥春,刘志毅,邵成伟. 焊接缺陷对异种铝合金TIG对接接头疲劳行为的影响. 北京航空航天大学学报. 2021(07): 1505-1514 . 百度学术
    8. 韦旭,汪建利,汪洪峰. 5052铝合金搅拌摩擦焊接的组织和力学性能. 兵器材料科学与工程. 2020(04): 77-80 . 百度学术
    9. 马青娜,邵飞,白林越,徐倩. 7075铝合金FSW接头腐蚀疲劳性能及断裂特征. 焊接学报. 2020(06): 72-77+101 . 本站查看
    10. 杨立恒,刘建军,张建国,陈大兵,李成钢. 浅析母线伸缩节焊接接头质量. 焊接技术. 2020(S1): 156-160 . 百度学术
    11. 张铁浩,杨志斌,张志毅,张海军,史春元. MIG焊叠加对6A01-T5铝合金FSW焊接头组织及性能的影响. 焊接学报. 2020(09): 81-88+96+101 . 本站查看

    其他类型引用(5)

计量
  • 文章访问数:  602
  • HTML全文浏览量:  11
  • PDF下载量:  5
  • 被引次数: 16
出版历程
  • 收稿日期:  2017-05-03

目录

    /

    返回文章
    返回