contributor author | Li, C. | |
contributor author | Liu, Z. Y. | |
contributor author | Fang, X. Y. | |
contributor author | Guo, Y. B. | |
date accessioned | 2019-02-28T11:02:30Z | |
date available | 2019-02-28T11:02:30Z | |
date copyright | 2/13/2018 12:00:00 AM | |
date issued | 2018 | |
identifier issn | 1087-1357 | |
identifier other | manu_140_04_041013.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4252011 | |
description abstract | Rapid heating and cooling thermal cycle of metals in selective laser melting (SLM) generates high tensile residual stress which leads to part distortion. However, how to fast and accurately predict residual stress and the resulted part distortion remains a critical issue. It is not practical to simulate every single laser scan to build up a functional part due to the exceedingly high computational cost. Therefore, scaling up the material deposition rate via increasing heat source dimension and layer thickness would dramatically reduce the computational cost. In this study, a multiscale scalable modeling approach has been developed to enable fast prediction of part distortion and residual stress. Case studies on residual stress and distortion of the L-shaped bar and the bridge structure were presented via the deposition scalability and validation with the experimental data. High residual stress gradient in the building direction was found from high tensile on the surface to high compressive in the core. The part distortion can be predicted with reasonable accuracy when the block thickness is scaled up by 50 times the layer thickness from 30 μm to 1500 μm. The influence of laser scanning strategy on residual stress distribution and distortion magnitude of the bridges has shown that orthogonal scanning pattern between two neighboring block layers is beneficial for reducing part distortion. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | On the Simulation Scalability of Predicting Residual Stress and Distortion in Selective Laser Melting | |
type | Journal Paper | |
journal volume | 140 | |
journal issue | 4 | |
journal title | Journal of Manufacturing Science and Engineering | |
identifier doi | 10.1115/1.4038893 | |
journal fristpage | 41013 | |
journal lastpage | 041013-10 | |
tree | Journal of Manufacturing Science and Engineering:;2018:;volume( 140 ):;issue: 004 | |
contenttype | Fulltext | |