Numerical and Experimental Investigations on Deposition of Stainless Steel in Wire Arc Additive Manufacturing

1. Introduction

Wire arc additive manufacturing (WAAM) process is a welding-based additive manufacturing process and comes under the directed energy deposition (DED) processes (Pan et al., 2018). In wire and arc based AM, metailic wire materials are melted by an arc heat source, and the melted materials are accumulated on a substrate in layer by layer fashion. Different types of welding arcs, i.e., metal inert gas, tungsten inert gas, plasma arc, cold metal transfer etc. are utilized and welding torch motion is controlled by a computer numerical control/robot system (Williams et al., 2016). This process has potential applications in various industries due to its advantages of high deposition rate, low equipment costs and suitability for large sized components compared to the laser/electron beam and powder based additive manufacturing processes (Williams et al., 2016; Cunningham et al., 2018). Manufacturing of an engineering component by WAAM requires depositions of multiple layers up to the desired shape using a continuous wire feeding system with continuous involvement of an arc heat source undergoing a cycle of quick warming, liquefying, solidifying, and cooling throughout the process. The repeated heating and cooling cycles generate thermal gradients in the deposited components that induced undesirable levels of thermal stress and cause critical mechanical and microstructural effects on WAAM components (Wu et al., 2018). Modeling and analysis of the WAAM process is essentially required to identify suitable processing conditions in monitoring and control of the process for production of sound engineering parts (Xia et al., 2020). The WAAM process recently has received considerable attention among researchers, and investigations carried out in past on its different aspects are discussed below.

Thermomechanical analysis of WAAM process need heat source modeling and weld pool shape for temperature distributions in arc welding processes. Goldak et al. (1984) carried out experimental and numerical analysis to propose a double ellipsoidal heat source model for analyzing temperature distribution in arc welding process. Kim et al. (1995) studied the effects of various physical forces that controlled the weld pole heat transfer and the molten metal flow in the MIG welding-based deposition process. Chiumenti et al. (2010) investigated the effects of temperature distribution on WAAM processed Inconel 718 alloy components through FEM and experimental analysis. The purposed model was validated through experimental results with good accuracy. Ding et al. (2011) proposed a steady-state FEM model to predict stress redistribution and distortions induced during the WAAM process. The developed FEM model was validated by depositing a multi-layer wall experimentally. Experimental study conducted by Ding et al (2014) revealed that the residual stress at a location was dictated by the peak temperatures experienced by that location during thermal cycles of WAAM process. Michaleris et al. (2014) investigated quiet and inactive element activation techniques to minimize errors associated with the element activation in FEM simulation of WAAM process. Fu et al. (2015) developed an algorithm based on neural network to predict the process parameters of Goldak’s double-ellipsoidal heat source model. The proposed algorithm was validated experimentally through multi-pass welding and found good agreement with the predicted process parameters. Montevecchi et al. (2016) carried out finite element simulations of material deposition in WAAM considering both the deposited material and base plate material. The new heat source model used in their analysis gave better results compared to Goldak heat source model. Wang et al. (2016) investigated the effects of fixing the FEM model to reduce distortion defects in WAAM process. It was found that the maximum effect of stress distributions was near to the clamping region.