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    Probabilistic Printability Maps for Laser Powder Bed Fusion Via Functional Calibration and Uncertainty Propagation

    Source: Journal of Computing and Information Science in Engineering:;2024:;volume( 024 ):;issue: 011::page 111001-1
    Author:
    Wu, Nicholas
    ,
    Whalen, Brendan
    ,
    Ma, Ji
    ,
    Balachandran, Prasanna V.
    DOI: 10.1115/1.4063727
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: In this work, we develop an efficient computational framework for process space exploration in laser powder bed fusion (LPBF) based additive manufacturing technology. This framework aims to find suitable processing conditions by characterizing the probability of encountering common build defects. We employ a Bayesian approach toward inferring a functional relationship between LPBF processing conditions and the unobserved parameters of laser energy absorption and powder bed porosity. The relationship between processing conditions and inferred laser energy absorption is found to have good correspondence to the literature measurements of powder bed energy absorption using calorimetric methods. The Bayesian approach naturally enables uncertainty quantification and we demonstrate its utility by performing efficient forward propagation of uncertainties through the modified Eagar–Tsai model to obtain estimates of melt pool geometries, which we validate using out-of-sample experimental data from the literature. These melt pool predictions are then used to compute the probability of occurrence of keyhole and lack-of-fusion based defects using geometry-based criteria. This information is summarized in a probabilistic printability map. We find that the probabilistic printability map can describe the keyhole and lack-of-fusion behavior in experimental data used for calibration, and is capable of generalizing to wider regions of processing space. This analysis is conducted for SS316L, IN718, IN625, and Ti6Al4V using melt pool measurement data retrieved from the literature.
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      Probabilistic Printability Maps for Laser Powder Bed Fusion Via Functional Calibration and Uncertainty Propagation

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4303184
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    contributor authorWu, Nicholas
    contributor authorWhalen, Brendan
    contributor authorMa, Ji
    contributor authorBalachandran, Prasanna V.
    date accessioned2024-12-24T19:02:26Z
    date available2024-12-24T19:02:26Z
    date copyright7/22/2024 12:00:00 AM
    date issued2024
    identifier issn1530-9827
    identifier otherjcise_24_11_111001.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4303184
    description abstractIn this work, we develop an efficient computational framework for process space exploration in laser powder bed fusion (LPBF) based additive manufacturing technology. This framework aims to find suitable processing conditions by characterizing the probability of encountering common build defects. We employ a Bayesian approach toward inferring a functional relationship between LPBF processing conditions and the unobserved parameters of laser energy absorption and powder bed porosity. The relationship between processing conditions and inferred laser energy absorption is found to have good correspondence to the literature measurements of powder bed energy absorption using calorimetric methods. The Bayesian approach naturally enables uncertainty quantification and we demonstrate its utility by performing efficient forward propagation of uncertainties through the modified Eagar–Tsai model to obtain estimates of melt pool geometries, which we validate using out-of-sample experimental data from the literature. These melt pool predictions are then used to compute the probability of occurrence of keyhole and lack-of-fusion based defects using geometry-based criteria. This information is summarized in a probabilistic printability map. We find that the probabilistic printability map can describe the keyhole and lack-of-fusion behavior in experimental data used for calibration, and is capable of generalizing to wider regions of processing space. This analysis is conducted for SS316L, IN718, IN625, and Ti6Al4V using melt pool measurement data retrieved from the literature.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleProbabilistic Printability Maps for Laser Powder Bed Fusion Via Functional Calibration and Uncertainty Propagation
    typeJournal Paper
    journal volume24
    journal issue11
    journal titleJournal of Computing and Information Science in Engineering
    identifier doi10.1115/1.4063727
    journal fristpage111001-1
    journal lastpage111001-12
    page12
    treeJournal of Computing and Information Science in Engineering:;2024:;volume( 024 ):;issue: 011
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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