contributor author | Kousoulas, Panayiotis | |
contributor author | Guo, Y. B. | |
date accessioned | 2025-04-21T10:19:54Z | |
date available | 2025-04-21T10:19:54Z | |
date copyright | 10/24/2024 12:00:00 AM | |
date issued | 2024 | |
identifier issn | 1087-1357 | |
identifier other | manu_147_2_021009.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4305959 | |
description abstract | Laser powder bed fusion (LPBF) is an enabling process manufacture of complex metal components. However, LPBF is prone to generate geometrical defects (e.g., porosity, lack of fusion), which causes a significant fatigue scattering. However, LPBF fatigue scattering data and analysis in the literature are not only sparse and limited to tension-compression mode but also inconsistent. This article presents a robust high-frequency fatigue testing method to construct stress-cycle curves of SS 316L to understand the scattering nature and predict the scattering pattern. A series of bending fatigue tests are performed at different stress amplitudes. Two different runout criteria are used to investigate fatigue life, fatigue limits, and scattering. The endurance limit reaches around 300 MPa for the defect size distribution at the selected process space. The defect size-based fatigue limit model is found to underestimate the endurance limit by about 30 MPa when comparing with the experimental data. Fatigue scattering is further calculated by using 95% prediction intervals, showing that low fatigue scattering is present at high stresses while a large variation of fatigue life occurs at stresses near the knee point. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | Fatigue Scattering Analytics and Prediction of SS 316L Fabricated by Laser Powder Bed Fusion | |
type | Journal Paper | |
journal volume | 147 | |
journal issue | 2 | |
journal title | Journal of Manufacturing Science and Engineering | |
identifier doi | 10.1115/1.4066803 | |
journal fristpage | 21009-1 | |
journal lastpage | 21009-9 | |
page | 9 | |
tree | Journal of Manufacturing Science and Engineering:;2024:;volume( 147 ):;issue: 002 | |
contenttype | Fulltext | |