Control of weldpool width and cooling time in TIG welding using a neural network model

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Abstract
Good weld quality requires control of weldpool width and cooling time. The cooling time t8/5 (from 800 °C to 500 °C) is related to the weld metal hardness. The weld metal is affected by rapid heating and rapid cooling, with various metallographic structures being formed. An important factor is appropriate selection of the cooling time to obtain a good structure and suitable strength after welding. The cooling time is strongly affected by the heat input (arc voltage U, welding current I, welding speed S, and heat input efficiency η). Numerical simulations of TIG pipe welding were initially performed by the finite difference method on the basis of heat conduction theory. The weldpool width and cooling time were then calculated at various welding currents and welding speeds. A weldpool model was constructed by a neural network using the above data as teaching signals. The welding speed and welding current were determined to obtain the required weldpool width and cooling time using the neural network model.

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