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Back-bead Prediction and Weldability Estimation Using An Artificial Neural Network  

Lee, Jeong-Ick (인하공업전문대학 기계시스템학부 기계설계과)
Koh, Byung-Kab (인하공업전문대학 기계시스템학부 기계과)
Publication Information
Transactions of the Korean Society of Machine Tool Engineers / v.16, no.4, 2007 , pp. 79-86 More about this Journal
Abstract
The shape of excessive penetration mainly depends on welding conditions(welding current and welding voltage), and welding process(groove gap and welding speed). These conditions are the major affecting factors to width and height of back bead. In this paper, back-bead prediction and weldability estimation using artificial neural network were investigated. Results are as follows. 1) If groove gap, welding current, welding voltage and welding speed will be previously determined as a welding condition, width and height of back bead can be predicted by artificial neural network system without experimental measurement. 2) From the result applied to three weld quality levels(ISO 5817), both experimented measurement using vision sensor and predicted mean values by artificial neural network showed good agreement. 3) The width and height of back bead are proportional to groove gap, welding current and welding voltage, but welding speed. is not.
Keywords
Excessive penetration; Prediction of back-bead; Assessment of weldability; Segment splitting method; Artificial neural network; Backpropagation; Back-bead width; Back-bead Height;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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