Analysis of material property models on WAAM distortion using nonlinear numerical computation and experimental verification with P-GMAW

This fundamental research deals with the investigation of material property model influences on distortion induced by multi-layered Wire Arc Additive Manufacturing (WAAM) with synergic-pulsed gas metal arc welding (P-GMAW) process which was modelled and simulated by means of non-linear numerical computation. The material property models of stainless steel SS316L component to be compared stem from three different sources namely existing database, initial wire and evolved component. The new property models were generated with advanced material modelling software JMATPRO based on chemical compositions analysed at initial wire and component using SEM–EDX. The flow curve for each material model was taken with the strain rates ranging from 0.001 to 1.0 s−1. In the numerical simulation, a coupled thermomechanical solution was adopted including phase-change phenomena defined in latent heat. Goldak’s double ellipsoid was applied as heat source model and simplified rectangular bead with hexagonal element type and meshing was developed to avoid extensive pre-processing effort and to reduce the computational time at post-processing level. Temperature behaviour due to the successive layer deposition was simulated considering heat transfer effect coupled to mechanical analysis. The adjustment of simulative transient to experimental thermal distribution lead to new fitted heat transfer coefficient. Prior to execution of numerical simulation, a sensitivity analysis was conducted to find the optimal number of elements or mesh size towards maximum reached temperature. It can be concluded based on the adjusted model, selected mesh size and experimental validation that numerical computation of substrate distortion with evolved material property of component and initial wire of SS316L yield closer average result within the relative error ranging between 11 and 16% compared to database material giving more than 22%.