Advanced Search
GAO Xiangdong, LIN Jun, XIAO Zhenlin, CHEN Xiaohui. Recognition model of arc welding penetration using ICA-BP neural network[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2016, 37(5): 33-36.
Citation: GAO Xiangdong, LIN Jun, XIAO Zhenlin, CHEN Xiaohui. Recognition model of arc welding penetration using ICA-BP neural network[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2016, 37(5): 33-36.
shu

Recognition model of arc welding penetration using ICA-BP neural network

  • 1.

    Guangdong Provincial Key Laboratory of Computer Integrated Manufacturing, School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China

  • 2.

    Guangzhou Panyu Gofront Dyeing & Finishing Machinery Manufacturer Ltd., Guangzhou 511400, China

More Information
  • Received Date: October 30, 2014
    Graphical Abstract
    • Abstract
  • A BP neural network model based on ICA (Imperialist Competitive Algorithm) is proposed to recognize the arc welding penetration status. The weights and thresholds of the neural network are initialized using ICA which has the features of uneasy accessibility to local extremum and fast search speed. Then the BP algorithm is used to train the neural network. By capturing the images of the molten pool in welding process, three features of a molten pool image are processed. The features includes the weld pool area, weld pool width and the distance between the weld pool centroid and the bottom. These features are as the inputs of neural network to create the mapping relationship between the three features of molten pool and the weld penetration status, and eventually a predicted model of penetration status is established. Welding experimental results show that the welding penetration status can be accurately recognized using the ICA-BP neural network.
    • FullText(HTML)
    • References (6)
  • 高向东, 江良征, 龙观富. 强弧光反射环境下频域滤波的熔宽特征提取[J]. 焊接学报, 2013, 34(8): 5-8. Gao Xiangdong, Jiang Liangzheng, Long Guanfu. Detection of welding pool width with frequency domain filtering in strong arc reflection environment[J]. Transactions of the China Welding Institution, 2013, 34(8): 5-8.
    杨友文, 田宗军, 潘 浒, 等. 基于遗传神经网络的镍基高温合金激光熔覆层形貌质量预测[J]. 焊接学报, 2013. 11, 34(11): 78-82. Yang Youwen, Tian Zongjun, Pan Hu, et al. Geometry quality prediction of Ni-based superalloy coating by laser cladding based on neural network and genetic algorithm[J]. Transactions of the China Welding Institution, 2013. 11, 34(11): 78-82.
    吴松坪, 王春明, 胡伦骥, 等. 激光焊接典型熔透状态信号特征分析及其识别[J]. 焊接学报, 2006.7, 27(7):69-73. Wu Songping, Wang Chunming, Hu Lunji, et al. Characteristic signal analysis and its identification of typical weld penetration status in laser welding[J]. Transactions of the China Welding Institution, 2006.7, 27(7): 69-73.
    Yu H W, Ye Z, Chen S B. Application of arc plasma spectral information in the monitor of Al-Mg alloy pulsed GTAW penetration status based on fuzzy logic system[J]. The International Journal of Advanced Manufacturing Technology, 2013.3(3): 2713-2727.
    Duan H B, Huang L Z. Imperialist competitive algorithm optimized artificial neural networks for UCAV global path planning[J]. Neurocomputing, 2014, 125: 166-171.
    Taghavifar H, Mardani A, Taghavifar L. A hybridized artificial neural network and imperialist competitive algorithm optimization approach for prediction of soil compaction in soil bin facility[J]. Measurement, 2013, 46: 2288-2299.
    • Related Articles

  • Related Articles

    [1]WANG Tianqi, MENG Kaiquan, WANG Chuanrui. Prediction and optimization of multi-layer and multi-pass welding process parameters based on GA-BP neural network[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(5): 29-37. DOI: 10.12073/j.hjxb.20230523001
    [2]LI Chengwen, JI Haibiao, YAN Zhaohui, LIU Zhihong, MA Jianguo, WANG Rui, WU Jiefeng. Prediction of residual stress and deformation of 316L multi-layer multi-pass welding based on GA-BP neural network[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(5): 20-28. DOI: 10.12073/j.hjxb.20230520002
    [3]DONG Jianwei, HU Jianming, LUO Zhen. Quality prediction of aluminum alloy resistance spot welding based on correlation analysis and SSA-BP neural network[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(2): 13-18, 32. DOI: 10.12073/j.hjxb.20230226001
    [4]TANG Quan, SHI Zhixin, MAO Zhiwei. Spatter analysis of rotating arc image based on multi threshold and neural network[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2022, 43(12): 41-46. DOI: 10.12073/j.hjxb.20211219001
    [5]GAO Changlin, SONG Yanli, ZUO Hongzhou, ZHANG Cheng. Cause diagnosis of welding defects based on adaptive PSO-BP neural network with dynamic weighting[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2022, 43(1): 98-106. DOI: 10.12073/j.hjxb.20210515001
    [6]LUO Liuxiang, XING Yanfeng. CMT spot welding deformation of sheet metal based on BP neural network and genetic algorithm[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2019, 40(4): 79-83. DOI: 10.12073/j.hjxb.2019400104
    [7]YU Guo, YIN Yuhuan, GAO Jiashuang, GUO Lijie. Orthogonal experiment method and BP neural networks in optimization of microbeam TIG welded GH4169[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2018, 39(11): 119-123. DOI: 10.12073/j.hjxb.2018390285
    [8]ZHANG Aihua, GAO Folai, NIU Xiaoge, LUO Huan. Prediction of gray-spot area in rail flash butt welded joint based on BP neural network[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2016, 37(11): 11-14.
    [9]ZHOU Jianping, XU Yan, CAO Jiong, YIN Yiliang, XU Yihao. High power supply optimization design based on BP neural network and genetic algorithm[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2016, 37(4): 9-13.
    [10]LEI Yu cheng, LIU Wei, CHENG Xiao nong. BP Neural Network Predicting Model for Aluminium Alloy Keyhole Plasma Arc Welding in Vertical Position[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2002, (6): 41-43.

  • Cited by

    Periodical cited type(5)

    1. 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 .
    2. 冯志强，袁浩，刘鹏，向晓宏，曾宪平，黎欣，黎泉. 变间隙MAG焊熔透状态的粗糙-模糊控制方法. 焊接学报. 2023(11): 22-35+130-131 . 本站查看
    3. 金伟，赵红霞，郭于明. 熔透状态识别预测与控制技术研究现状. 中国工程机械学报. 2021(03): 217-221 .
    4. 任乐，张爱华，常东东，何倩玉，马晶. 基于电弧声的超窄间隙焊接熔合状态识别. 电焊机. 2021(07): 11-16+23+113-114 .
    5. 黄军芬，薛龙，黄继强，邹勇，马可，焦向东. 基于视觉传感的GMAW熔透状态预测. 机械工程学报. 2019(17): 41-47 .

    Other cited types(7)

Catalog

    Get Citation
    PDF
    XML
    Article views (428) PDF downloads (358) Cited by(12)
    Turn off MathJax
    Article Contents
    Abstract
    References
    Related Articles

    Website Copyright © Editorial Office of Transactions of the China Welding Institution, Welding Journals Publishing HouseTel：0451-86323218Address：No. 2077, Chuangxin Road, Songbei District, Harbin

    Supported by: Beijing Renhe Information Technology Co. Ltd    Baidu Analytics

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return