Estimate of process parameters in additive manufacturing with artificial intelligence methods

Wire Arc Additive Manufacturing (WAAM), one of the Additive Manufacturing (AM) methods, is a manufacturing method that allows materials to be part produced by deposited them in layers. In WAAM, welding arc is used as heat source and welding wire is used as feeding material. Large structural parts can be produced with this manufacturing method, which has a high deposition rate. With the advantages it provides, it attracts the attention of sectors such as space, aviation and shipping. The process parameters selected in WAAM affect the quality, mechanical and microstructure properties of the produced part. Estimating the area, width and length of the weld pool depending on the selected parameters gives important information about the quality of the process. In this thesis, the area, width and length of the melting pool against the process parameters were estimated by artificial neural networks (ANN). Wire feed speed (WFS), welding current, welding voltage and torch travel speed (TTS) were used as input parameters of ANN.