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    Layer-to-Layer Height Control for Laser Metal Deposition Process

    Source: Journal of Manufacturing Science and Engineering:;2011:;volume( 133 ):;issue: 002::page 21009
    Author:
    Lie Tang
    ,
    Robert G. Landers
    DOI: 10.1115/1.4003691
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: A 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.
    keyword(s): Flow (Dynamics) , Metals , Lasers , Algorithms , Particle swarm optimization , Iterative learning control , Simulation , Control equipment AND Temperature ,
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      Layer-to-Layer Height Control for Laser Metal Deposition Process

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    http://yetl.yabesh.ir/yetl1/handle/yetl/146906
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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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