Citation: | FENG Zhiqiang, YUAN Hao, LIU Peng, Xiang Xiaohong, Zeng Xianping, LI Xin, LI Quan. A rough-fuzzy control method for the penetration state of variable gap MAG welding[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2023, 44(11): 22-35. DOI: 10.12073/j.hjxb.20230407003 |
There is a certain correspondence between the frontal molten pool and the penetration state. However, due to the numerous potential weld pool features related to the penetration state, how to remove redundant features and establish a concise knowledge model that reflects the corresponding relationship between weld pool features and penetration state is of great significance for achieving online control of welding penetration. A rough-fuzzy control method for welding penetration state is proposed and verified in variable gap MAG welding experiments. To solve the minimal feature set of the melt pool that characterizes the penetration state, we provide two attribute reduction algorithms based on variable precision rough sets. A decision information system for penetration state is established through variable gap-current welding experiments, and two shape parameters are introduced to describe the degree of sharpness at the tail of the molten pool. The classification rules of penetration status are obtained using rough set knowledge reduction and rule extraction algorithms. We establish a fuzzy control model for the width coefficient of the molten pool tail, and use the minimization of fuzzy entropy to construct membership functions of the error domain. The proposed control model is validated through two sets of variable gap welding experiments, and the results show that under closed-loop control, the weld back width is uniform and consistent, which can meet the requirements of welding specifications.
[1] |
殷树言. 气体保护焊工艺基础[M]. 北京: 机械工业出版社, 2007.
Yin Shuyan. Basic technology of gas shielded welding[M]. Beijing: China Machine Press, 2007.
|
[2] |
吴林, 陈善本. 弧焊机器人智能化技术[M]. 北京: 国防工业出版社, 2000.
Wu Lin, Chen Shanben. Intelligent technology of arc welding robot[M]. Beijing: National Defense Industry Press, 2000.
|
[3] |
Feng Yanzhu, Gao Xiongdong, Zhang Yanxi, et al. Simulation and experiment for dynamics of laser welding keyhole and molten pool at different penetration status[J]. International Journal of Advanced Manufacturing Technology, 2021, 112: 2301 − 2312.
|
[4] |
Ye Guangwen, Guo Xiangdong, Liu Qianwen, et al. Prediction of weld back width based on top vision sensing during laser-MIG hybrid welding[J]. Journal of Manufacturing Processes, 2022, 84: 1376 − 1388.
|
[5] |
杨嘉佳, 王克鸿, 吴统立, 等. 基于熔池视觉特征的铝合金双丝焊熔透识别[J]. 焊接学报, 2017, 38(3): 49 − 52.
Yang Jiajia, Wang Kehong, Wu Tongli, et al. Welding penetration recognition in aluminum alloy tandom arc welding based on visual characters of weld pool[J]. Transactions of the China Welding Institution, 2017, 38(3): 49 − 52.
|
[6] |
林俊, 高向东. 电弧焊熔池表征与熔透状态映射研究[J]. 焊接, 2016(10): 34 − 37. doi: 10.3969/j.issn.1001-1382.2016.10.008
Lin Jun, Gao Xiangdong. Mapping relationship between weld pool surface feature and weld penetration during arc welding[J]. Welding & Joining, 2016(10): 34 − 37. doi: 10.3969/j.issn.1001-1382.2016.10.008
|
[7] |
高向东, 林俊, 萧振林, 等. 电弧焊熔透ICA-BP神经网络识别模型[J]. 焊接学报, 2016, 37(5): 33 − 36.
Gao Xiangdong, Lin Jun, Xiao Zhenlin, et al. Recognition model of arc welding penetration using ICA-BP neural network[J]. Transactions of the China Welding Institution, 2016, 37(5): 33 − 36.
|
[8] |
刘文焕, 王克争, 何方殿. 人工神经网络控制在焊接中的研究应用[J]. 电焊机, 1997(5): 15 − 18.
Liu Wenhuan, Wang Kezheng, He Fangdian. Development and application of artificial neural network in welding[J]. Electric Welding Machine, 1997(5): 15 − 18.
|
[9] |
陈善本, 娄亚军, 赵冬斌, 等. 脉冲GTAW熔池动态过程模糊神经网络建模与控制[J]. 自动化学报, 2002, 28(1): 74 − 82. doi: 10.16383/j.aas.2002.01.010
Chen Shanben, Lou Yajun, Zhao Dongbin, et al. Fuzzy-neural network modeling and control of pool dynamic process in pulsed GTAW[J]. Acta Automatica Sinica, 2002, 28(1): 74 − 82. doi: 10.16383/j.aas.2002.01.010
|
[10] |
张勇, 陈善本, 邱涛, 等. 焊接柔性加工单元中熔池的实时控制[J]. 焊接学报, 2002, 23(4): 1 − 5. doi: 10.3321/j.issn:0253-360X.2002.04.001
Zhang Yong, Chen Shanben, Qiu Tao, et al. Study on real-time control of welding pool in welding flexible manufacturing cell[J]. Transactions of the China Welding Institution, 2002, 23(4): 1 − 5. doi: 10.3321/j.issn:0253-360X.2002.04.001
|
[11] |
徐中路, 李静, 陈丹, 等. 基于受限波尔兹曼机的GMAW管道打底焊的熔透预测方法[J]. 计算机应用与软件, 2013, 30(10): 239 − 242. doi: 10.3969/j.issn.1000-386x.2013.10.066
Xu Zhonglu, Li Jing, Chen Dan, et al. A weld penetration prediction method based on RBM for GMAW pipe-line backing welding[J]. Computer Applications andSoftware, 2013, 30(10): 239 − 242. doi: 10.3969/j.issn.1000-386x.2013.10.066
|
[12] |
刘亮, 杨长祺, 倪加明, 等. 2219铝合金变极性TIG焊熔透状态识别方法[J]. 上海交通大学学报, 2016, 50(Suppl.1): 71 − 74. doi: 10.16183/j.cnki.jsjtu.2016.S.018
Liu Liang, Yang Changqi, Ni Jiaming, et al. Recognition method research for variable polarity TIG welding penetration state of 2219 aluminum alloy[J]. Journal of Shanghai Jiao Tong University, 2016, 50(Suppl.1): 71 − 74. doi: 10.16183/j.cnki.jsjtu.2016.S.018
|
[13] |
Feng Y, Chen Z, Wang D, et al. Deep welding: A deep learning enhanced approach to GTAW using multisource sensing images[J]. IEEE Transactions on Industrial Informatics, 2020, 16(1): 465 − 474. doi: 10.1109/TII.2019.2937563
|
[14] |
Jiao W, Wang Q, Cheng Y, et al. End-to-end prediction of welding penetration: A deep learning and transfer learning based method[J]. Journal of Manufacturing processes, 2021, 63: 191 − 197. doi: 10.1016/j.jmapro.2020.01.044
|
[15] |
Cai W, Jiang P, Shu L S, et al. Real-time monitoring of laser keyhole welding penetration state based on deep belief network[J]. Journal of Manufacturing processes, 2021, 72: 203 − 214. doi: 10.1016/j.jmapro.2021.10.027
|
[16] |
Niu Yue, Gao Peng P, Gao Xiangdong. Recognition of DC01 mild steel laser welding penetration status based on photoelectric signal and neural network[J]. Metals. 2023, 13, 871: 1 − 17.
|
[17] |
Xiong J, Zou S. Active vision sensing and feedback control of back penetration for thin sheet aluminum alloy in pulsed MIG suspension welding[J]. Journal of Process Control, 2019, 77: 89 − 96. doi: 10.1016/j.jprocont.2019.03.013
|
[18] |
王万东, 王志江, 胡绳荪, 等. 基于模型的GMAW-P焊接熔深自适应预测控制[J]. 机械工程学报, 2019, 55(19): 138 − 145. doi: 10.3901/JME.2019.19.138
Wang Wandong, Wang Zhijiang, Hu Shengsun, et al. Adaptive predictive control of weld penetration depth based on Hammerstein model in pulsed gas metal arc welding[J]. Journal of Mechanical Engineering, 2019, 55(19): 138 − 145. doi: 10.3901/JME.2019.19.138
|
[19] |
吴頓. 基于多源信息融合的铝合金VPPAW成形预测和智能控制研究[D]. 上海: 上海交通大学, 2018.
Wu Dun. Research on predicting and intelligent control for weld formation during VPPAW process using multi-information fusion[D]. Shanghai: Shanghai Jiao Tong University, 2018.
|
[20] |
Pawlak Z. Rough sets: Theoretical aspects of reasoning about data. Dordrecht: Kluwer Academic Publishing, 1991.
|
[21] |
王国胤, 姚一豫, 于洪. 粗糙集理论与应用研究综述[J]. 计算机学报, 2009, 32(7): 1229 − 1246. doi: 10.3724/SP.J.1016.2009.01229
Wang Guoyin, Yao Yiyu, Yu Hong. A survey on rough set theory and its application[J]. Chinese Journal of Computers, 2009, 32(7): 1229 − 1246. doi: 10.3724/SP.J.1016.2009.01229
|
[22] |
Wang B, Chen S B, Wang J J. Rough set based knowledge modeling for the aluminum alloy pulsed GTAW process[J]. The International Journal of Advanced Manufacturing Technology, 2005, 25(9): 902 − 908.
|
[23] |
黎文航, 陈善本, 王加友, 等. 基于变精度粗糙集的脉冲GTAW过程建模方法[J]. 焊接学报, 2008, 29(7): 57 − 59. doi: 10.3321/j.issn:0253-360X.2008.07.015
Li Wenhang, Chen Shanben, Wang Jiayou, et al. Modeling method for pulsed GTAW welding process based on variable precision rough set[J]. Transactions of the China Welding Institution, 2008, 29(7): 57 − 59. doi: 10.3321/j.issn:0253-360X.2008.07.015
|
[24] |
Feng Zhiqiang, Liu Cungen, Huang Hu. Knowledge modeling based on interval valued fuzzy rough sets and similarity-based inference: prediction of welding distortion[J]. Journal of Zhejiang University-Science C, 2014, 15(8): 636 − 650. doi: 10.1631/jzus.C1300370
|
[25] |
Ziarko W. Variable precision rough set model[J]. Journal of Computer and System Sciences, 1993, 46(1): 39 − 59. doi: 10.1016/0022-0000(93)90048-2
|
[26] |
An A, Shan N, Chan C, et al. Discovering rules for water demand prediction: An enhanced rough-set approach[J]. Engineering Application and Artificial Intelligence, 1996, 9(6): 645 − 653. doi: 10.1016/S0952-1976(96)00059-0
|
[27] |
王国胤, 于洪, 杨大春. 基于条件信息熵的决策表约简[J]. 计算机学报, 2002, 25(7): 759 − 766. doi: 10.3321/j.issn:0254-4164.2002.07.013
Wang Guoyin, Yu Hong, Yang Dachu. Decision table reduction based on conditional information entropy[J]. Chinese Journal of Computers, 2002, 25(7): 759 − 766. doi: 10.3321/j.issn:0254-4164.2002.07.013
|
[28] |
于锟, 刘知贵, 黄正良. 粗糙集理论应用中的离散化方法综述[J]. 西南科技大学学报, 2005, 20(4): 32 − 36. doi: 10.3969/j.issn.1671-8755.2005.04.008
Yu Kun, Liu Zhigui, Huang Zhengliang. Overview of the discretization methods in the application of rough set theory[J]. Journal of Southwest University of Science and Technology, 2005, 20(4): 32 − 36. doi: 10.3969/j.issn.1671-8755.2005.04.008
|
[29] |
丁洁琼. MAG焊单面焊双面成形熔池视觉特征与控制模型研究[D]. 南京: 南京理工大学, 2010.
Ding Jieqiong. Research on characteristic of MAG weld pool visual image and control model in one-side welding with back formation weld [D]. Nanjing: Nanjing University of Science & Technology, 2010.
|
[30] |
Wang K H, Tang X C, Liu Y. Methods of visional detecting MAG weld pool information[J]. Transactions of Nonferrous Metals Society of China, 2005, 15(3): 369 − 374.
|
[31] |
杨伦标, 高英仪. 模糊数学原理及应用[M]. 广州: 华南理工大学出版社, 2005.
Yang Lunbiao, Gao Yingyi. Principle and application of fuzzy mathematics[M]. Guangzhou: South China University of Technology Press, 2005.
|
[32] |
章卫国, 模糊控制理论与应用[M]. 西安: 西北工业大学出版社, 2000.
Zhang Weiguo. Fuzzy control theory and application[M]. Xi’an: Northwestern Polytechnical University Press, 2000.
|
[1] | LAN Hu, ZHANG Huajun, CHEN Ajing, CHEN Shanben. Numerical simulation on dynamic process and thermal physical properties of narrow gap MAG vertical welding[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2015, 36(7): 77-82. |
[2] | WANG Lei, HUANG Songtao, JIAO Xiangdong, GU Xiaoman. Stability of hyperbaric pulsed MAG welding arc and its compensation by higher arc voltage[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2015, 36(3): 63-66. |
[3] | ZHENG Senmu, GAO Hongming, LIU Xin. Arc behavior of MAG welding with strip electrode[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2011, (10): 97-100. |
[4] | LI Jing, LI Fang, ZHU Wei, LIAO Jianxiong, QIAN Luhong. A new seam location extraction method for pipe-line backing welding of MAG based on passive optical vision sensor[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2011, (10): 69-72. |
[5] | LI Zhiyong, WANG Wei, WANG Xuyou, LI Huan. Analysis of laser-MAG hybrid welding plasma radiation[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2010, (3): 21-24,28. |
[6] | WEN Yuanmei, HUANG Shisheng, WU Kaiyuan, LAO Zhengping. Welding behavior of two current phase relations for twin-wire pulsed MAG welding[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2010, (1): 59-62,66. |
[7] | WANG Jia-you, GUO Hong-bin, YANG Feng. A new rotating arc process for narrow gap MAG welding[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2005, (10): 65-67. |
[8] | BAO Ye-feng, ZHOU Yun, WU Yi-xiong, LOU Song-nian. Instant unstable phenomenon of rotational spray transfer in high-current MAG welding[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2003, (6): 73-76. |
[9] | Sun Lunqiang, Wu Lin. A Regression Model of Weld Bead Geometry for Pulsed MAG Welding in Multipositions[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 1996, (4): 249-257. |
[10] | Jiang Weiyan, Zhang Jiuhai, Zhao Chongyi. Behaviors of metal transfer in pulsed MIG(MAG) welding[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 1994, (1): 50-58. |