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(12)
1. 周浩南，孙文磊，王伟，张志虎. 面向涂层裂纹的激光熔覆预测模型研究. 热加工工艺. 2024(14): 27-32 . 
百度学术
2. 郑世茂，刘玉国，王豪，王新佩，陈洪堂. 长直焊缝自动焊接设备研究. 南方农机. 2023(10): 127-128+154 . 百度学术
3. 王颖，高胜，吴立明. 基于胶囊网络的TIG熔透预测. 焊接. 2023(04): 15-20+28 . 百度学术
4. 黄威威，游德勇，高向东，张艳喜，黄宇辉. 基于相关分析和神经网络的激光焊接稳态识别. 激光技术. 2022(03): 312-319 . 百度学术
5. 吴月玉，张弓，林群煦，侯至丞，杨文林. 机器人TIG焊接的焊缝形貌遗传神经网络预测. 制造业自动化. 2022(07): 86-90 . 百度学术
6. 刘秀航，黄宇辉，张艳喜，高向东. 基于BP神经网络补偿卡尔曼滤波的激光-MIG复合焊缝熔宽在线检测. 中国激光. 2022(16): 115-121 . 百度学术
7. 陶永，兰江波，任帆，王田苗，江山，高赫，温宇方. 基于自适应模糊神经网络的机器人焊接焊缝外形预测方法. 计算机集成制造系统. 2022(11): 3643-3651 . 百度学术
8. 刘天元，鲍劲松，汪俊亮，顾俊. 融合时序信息的激光焊接熔透状态识别方法. 中国激光. 2021(06): 228-238 . 百度学术
9. 吴月玉，张弓，林群煦，侯至丞，杨文林，刘胜祥，徐群华，张雨航. 焊接机器人特征参数预测方法的研究综述与展望. 机床与液压. 2021(15): 168-173+199 . 百度学术
10. 成慧翔，马艳娥，李新卫. 基于改进神经网络的激光焊接偏差智能识别研究. 激光杂志. 2021(12): 165-169 . 百度学术
11. 火巧英，闫海宁，涂本荣，陆安进. 焊接工艺参数对Q345NQR2耐候钢激光焊焊缝成形的影响. 焊接技术. 2020(08): 16-18+105-106 . 百度学术
12. 范鹏飞，张冠. 基于线性回归和神经网络的金属陶瓷激光熔覆层形貌预测. 表面技术. 2019(12): 353-359+368 . 百度学术
其他类型引用(6)
计量
- 文章访问数: 605
- HTML全文浏览量: 11
- PDF下载量: 5
- 被引次数: 18
下载:
