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contributor authorLie Tang
contributor authorRobert G. Landers
date accessioned2017-05-09T00:45:32Z
date available2017-05-09T00:45:32Z
date copyrightApril, 2011
date issued2011
identifier issn1087-1357
identifier otherJMSEFK-28447#021009_1.pdf
identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/146906
description abstractA laser metal deposition height control methodology is presented in this paper. The height controller utilizes a particle swarm optimization (PSO) algorithm to estimate model parameters between layers using measured temperature and track height profiles. Using the estimated model, the powder flow rate reference profile, which will produce the desired layer height reference, is then generated using iterative learning control (ILC). The model parameter estimation performance using PSO is evaluated using a four-layer single track deposition, and the powder flow rate reference generation performance using ILC is tested using simulation. The results show that PSO and ILC perform well in estimating model parameters and generating powder flow rate references, respectively. The proposed height control methodology is then tested experimentally for tracking a constant height reference with constant traverse speed and constant laser power. The experimental results indicate that the controller performs well in tracking constant height references in comparison with the widely used fixed process parameter strategy. The application of layer-to-layer height control produces more consistent layer height increment and a more precise track height, which saves machining time and increases powder efficiency.
publisherThe American Society of Mechanical Engineers (ASME)
titleLayer-to-Layer Height Control for Laser Metal Deposition Process
typeJournal Paper
journal volume133
journal issue2
journal titleJournal of Manufacturing Science and Engineering
identifier doi10.1115/1.4003691
journal fristpage21009
identifier eissn1528-8935
keywordsFlow (Dynamics)
keywordsMetals
keywordsLasers
keywordsAlgorithms
keywordsParticle swarm optimization
keywordsIterative learning control
keywordsSimulation
keywordsControl equipment AND Temperature
treeJournal of Manufacturing Science and Engineering:;2011:;volume( 133 ):;issue: 002
contenttypeFulltext


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