Prediction and optimization of processing parameters in wire and arc-based additively manufacturing of 316L stainless steel

Wire and arc-based additively manufacturing (WAAM) is a potential metallic additively manufacturing (AM) technologies for producing large-size metallic components. 316L is one of the most common stainless-steel grades used in WAAM. However, most of previous studies normally adopted process parameters for the WAAM process based on recommendations of welding wire manufacturers for traditional welding processes. In this article, we focus on predicting and optimizing process parameters for the WAAM process of 316L stainless steel. The experiment was designed by using Taguchi method and L16 orthogonal array. Three parameters, consisting of voltage ( U ), welding current ( I ), and travel speed ( v ), were considered as the input variables, and the responses are four geometrical characteristics of single weld beads, including width, height, penetration, and dilution of weld beads (WWB, HWB, PWB, and DWB, respectively). The effects of each input variable on the responses were determined through analysis of variance (ANOVA). The optimal process parameters were identified by using GRA (grey-relational analysis) and TOPSIS (techniques for order-preferences by similarity-to-ideal solution) methods. The obtained results show that the travel speed has the most important effect on WWB and HWB, while the voltage has the highest impact on PWB and DWD. Both GRA and TOPSIS methods give the same optimum process parameters, namely U = 22 V, I = 110 A, and v = 0.3 m/min, which are validated by confirmation experiments. The predicted models of WWB, HWB, PWB, and DWB were also demonstrated to be adequate for selecting the process parameters in specific applications. Graphical abstract