Abstract
This paper presents a study on the weld quality obtained by different values of the input parameters. The weld quality is characterized by two categories of parameters: geometrical parameters and mechanical parameters. They are dependent on the following process parameters: electric arc voltage, electric current intensity, welding speed, the feed wire velocity. Because the dependence between inputs and outputs is a nonlinear one was used an artificial feed forward neural network (ANN). The ANN was trained with the backpropagation algorithm, using as training patterns data measured from the mechanical process. This ANN can be used to estimate some parameters from future experiments of the mechanical process.
Highlights
Automatic welding is wide used to build bridges, train wagons, tanks, pipes and other products
For the pieces assembled by automatic welding process, the manufacturer must guarantee the weld quality, from the point of view geometrical and structural
We cannot carry out experiments for the whole experimental field
Summary
Automatic welding is wide used to build bridges, train wagons, tanks, pipes and other products. The manufacturers must give a distinct attention to the quality and to the characteristics of the product. For the pieces assembled by automatic welding process, the manufacturer must guarantee the weld quality, from the point of view geometrical and structural. The analyse of the welding parameters effect over the quality indexes shows that only a part of them has a significant effect to be considered and studied. The weld quality is characterized by the geometry (B the maximum width of the welding cord [mm]; b - weld reinforcement [mm], HV10HAZ micro-hardness in the heat affected zone [HV]) and they are dependent on the following process parameters: Iw - current intensity [A], Ua - electric arc voltage [V], Sa - wire feed speed [cm / min], Ws - welding speed [cm / min]
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