In this work, an artificial neural network (ANN) was implemented to investigate the main effects of process parameters on the laser welding process quality. A high brightness Yb fiber laser was used to carry out the analysis. Full penetration autogenous welding of 6 mm thick AA5754 aluminum alloy sheets was performed in butt configuration. The welding speed and the shielding gas varied in the experimental plan. The process quality was analyzed by visually inspecting the bead appearance. The ANN modeling code was built by Neural Tools (Excel add-in) – Palisade Corporation®. The statistical estimation revealed the relationship of the process parameters with the weld geometry, which provides a deeper understanding of the welding process. Eventually, the usefulness of ANN modeling for optimizing the quality of manufacturing processes was demonstrated.
ANN modelling to optimize manufacturing processes: the case of laser welding
Casalino, G.;Facchini, F.;Mummolo, G.
2016-01-01
Abstract
In this work, an artificial neural network (ANN) was implemented to investigate the main effects of process parameters on the laser welding process quality. A high brightness Yb fiber laser was used to carry out the analysis. Full penetration autogenous welding of 6 mm thick AA5754 aluminum alloy sheets was performed in butt configuration. The welding speed and the shielding gas varied in the experimental plan. The process quality was analyzed by visually inspecting the bead appearance. The ANN modeling code was built by Neural Tools (Excel add-in) – Palisade Corporation®. The statistical estimation revealed the relationship of the process parameters with the weld geometry, which provides a deeper understanding of the welding process. Eventually, the usefulness of ANN modeling for optimizing the quality of manufacturing processes was demonstrated.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
