A validated analytical-numerical modelling strategy to predict residual stresses in single-track laser deposited IN718. (February 2019) - Walker, T.R.; Bennett, C.J.; Lee, T.L.; Clare, A.T.
Highlights: A validated analytical-numerical model of the DED process is presented. Finite element model uses realistic track geometry to predict residual stress and distortion. A modified catchment efficiency was utilised in the analytical model to allow a more realistic approach to be implemented. The maximum error for the peak temperature and residual stress was 3.1% and 18% respectively. Abstract: Direct Energy Deposition (DED) is being increasingly used to repair high value components that have been damaged in-service. The uptake of DED and laser cladding operations for repair is inhibited by accurate modelling techniques. Often the repair process required is unique, therefore modelling techniques are necessary to determine the process inputs for the specific application. The DED process subjects the component to high thermal gradients resulting in high magnitude residual stresses and component distortion. Prediction of these parameters would reduce the need for costly experimental trials to quantify the repair strategy. Here, a single-track deposition of IN718, utilising a Nd:YAG laser source and coaxial nozzle, was modelled using a semi analytical-numerical approach. The track profile, temperature fields, melt pool geometry and stress evolutions were simulated for a constant set of process parameters. A corresponding experimental trial was conducted to validate the proposed model, through the use of focus variation microscopy, in-situ temperature measurements, optical micrographs and neutron diffraction measurements. A good correlation between the experimental and numerical data sets were apparent. The track profile was predicted with a maximum error of 1.98% and 0.43% for the width and height respectively. The maximum error for the peak temperature and residual stress was 3.1% and 18% respectively. Overall, the modelling strategy presented encompasses the key process variables, allowing accurate predictions of the thermal and mechanical effects of the process. Graphical abstract: