• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.4
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• pp.79-86
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• 2007

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.

• Journal of the Korean Institute of Gas
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• v.8 no.3 s.24
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• pp.31-36
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• 2004

Electrofusion(EF) joints have been widely used as they are easy to fuse and suitable for high-quality joints for polyethylene(PE) pipes. This paper studies the cause of defects and classifies 5 types of defects. The defect detection technique for electrofusion joints of polyethylene piping is utilized by the ultrasonic phased array technique to obtain ultrasonic images of electrofusion joints. Test sample joints have been designed and fabricated using artificial defects which were made using paper. Finally, we studied the condition of electrofusion in the field and analyzed the main causes of defects. And we classified the defect types as local lack of fusion, sand inclusion, voids or air inclusion, short stab, excess penetration or excess bead.