contributor author | Katinas, Christopher | |
contributor author | Shang, Weixiao | |
contributor author | Shin, Yung C. | |
contributor author | Chen, Jun | |
date accessioned | 2019-02-28T11:02:22Z | |
date available | 2019-02-28T11:02:22Z | |
date copyright | 2/14/2018 12:00:00 AM | |
date issued | 2018 | |
identifier issn | 1087-1357 | |
identifier other | manu_140_04_041014.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4251992 | |
description abstract | Powder capture efficiency is indicative of the amount of material that is added to the substrate during laser additive manufacturing (AM) processes, and thus, being able to predict capture efficiency provides capability of predictive modeling during such processes. The focus of the work presented in this paper is to create a numerical model to understand particle trajectories and velocities, which in turn allows for the prediction of capture efficiency. To validate the numerical model, particle tracking velocimetry (PTV) experiments at two powder flow rates were conducted on free stream particle spray to track individual particles such that particle concentration and velocity fields could be obtained. Results from the free stream comparison showed good agreement to the trends observed in experimental data and were subsequently used in a direct laser deposition (DLD) simulation to assess capture efficiency and temperature profile at steady-state. The simulation was validated against a single track deposition experiment and showed proper correlation of the free surface geometry, molten pool boundary, heat affected zone boundary, and capture efficiency. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | Modeling Particle Spray and Capture Efficiency for Direct Laser Deposition Using a Four Nozzle Powder Injection System | |
type | Journal Paper | |
journal volume | 140 | |
journal issue | 4 | |
journal title | Journal of Manufacturing Science and Engineering | |
identifier doi | 10.1115/1.4038997 | |
journal fristpage | 41014 | |
journal lastpage | 041014-10 | |
tree | Journal of Manufacturing Science and Engineering:;2018:;volume( 140 ):;issue: 004 | |
contenttype | Fulltext | |