Predicting the depth of penetration and weld bead width from the infra red thermal image of the weld pool using artificial neural network modeling

被引:80
作者
Chokkalingham, S. [2 ]
Chandrasekhar, N. [1 ]
Vasudevan, M. [1 ]
机构
[1] Indira Gandhi Ctr Atom Res, Adv Welding Proc & Modeling Programme, Mat Technol Div, Kalpakkam 603102, Tamil Nadu, India
[2] PSG Coll Technol, Dept Prod Engn, Coimbatore, Tamil Nadu, India
关键词
Artificial neural network; Infra red thermal images; Image processing; Depth of penetration; Weld bead width; A-TIG welding;
D O I
10.1007/s10845-011-0526-4
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
It is necessary to estimate the weld bead width and depth of penetration using suitable sensors during welding to monitor weld quality. Among the vision sensors, infra red sensing is the natural choice for monitoring welding processes as welding is inherently a thermal processing method. An attempt has been made to estimate the weld bead width and depth of penetration from the infra red thermal image of the weld pool using artificial neural network models during A-TIG welding of 3 mm thick type 316 LN stainless steel plates. Real time infra red images were captured using IR camera for the entire weld length during A-TIG welding at various current values. The image features such as length and width of the hot spot, peak temperature, and other features using line scan analysis are extracted using image processing techniques corresponding to particular locations of the weld joint. These parameters along with their respective current values are used as inputs while the measured weld bead width and depth of penetration are used as output of the neural network models. Accurate ANN models predicting weld bead width (9-11-1) and depth of penetration (9-9-1) have been developed. The correlation coefficient values obtained were 0.98862 and 0.99184 between the measured and predicted values of weld bead width and depth of penetration respectively.
引用
收藏
页码:1995 / 2001
页数:7
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