Estimation of clad geometry and corresponding residual stress distribution in laser melting deposition: analytical modeling and experimental correlations

Abstract

Laser melting deposition (LMD) is a promising technology to produce net-shape parts. The deposited layers' characteristics and induced residual stress distribution influence the quality, mechanical, and physical properties of the manufactured parts. In this study, two theoretical models are presented. Initially, the clad geometry of the 1st deposited layer is estimated using the primary process parameters. Then, a hatch distance is used to calculate the re-melting depth and total clad geometry for all the deposited layers. The output of the 1st model is then used as an input in the 2nd model to estimate the residual stress distribution within the substrate and deposited layers. The model, for clad geometry, is validated using published experimental data for the depositions of AISI316L powder debits on AISI321 bulk substrate by the LMD process. For the residual stress distribution model validation, the published experimental results for X-ray diffractometry, in case of AISI4340 steel powder debits depositions on the AISI4140 bulk substrate by the LMD setup, are used. It was found that the current models can estimate the clad geometry and induced residual stress distribution with an accuracy of 10–15 % mean absolute deviation. An optimum selection of hatch distance is necessary for proper energy density utilization and dimensional control stability. The induced residual stress distribution was caused by the heating and cooling mechanisms, which appeared due to rapid heating and moderate cooling, in combination with slow conduction. These phenomena became incrementally iterative with the number of layers to be deposited, thus presenting a direct relationship between the residual stress distribution and the number of layers deposited on the substrate. The proposed models have high computational efficiency without restoring the meshing and iterative calculations. The high prediction accuracy and computational efficiency allow the presented model to investigate further the part distortion, part porosity, life-expectancy and mechanical properties of the part, and process parameter planning.

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