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

Content Provider	Springer Nature Link
Author	Chokkalingham, S. Chandrasekhar, N. Vasudevan, M.
Copyright Year	2011
Abstract	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.
Starting Page	1995
Ending Page	2001
Page Count	7
File Format	PDF
ISSN	09565515
Journal	Journal of Intelligent Manufacturing
Volume Number	23
Issue Number	5
e-ISSN	15728145
Language	English
Publisher	Springer US
Publisher Date	2011-03-23
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Artificial neural network Infra red thermal images Image processing Depth of penetration Weld bead width A-TIG welding Manufacturing, Machines, Tools Control, Robotics, Mechatronics Production/Logistics/Supply Chain
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Artificial Intelligence Software

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