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  • Moges, Tesfaye  (4)
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  • 1
    Online Resource
    Online Resource
    On Characterizing Uncertainty Sources in Laser Powder-Bed Fusion Additive Manufacturing Models
    ASME International ; 2022
    In:  ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg Vol. 8, No. 1 ( 2022-03-01)
    Details
    In: ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg, ASME International, Vol. 8, No. 1 ( 2022-03-01)
    Abstract: Tremendous efforts have been made to use computational and simulation models of additive manufacturing (AM) processes. The goals of these efforts are to better understand process complexities and to realize better high-quality parts. However, understanding whether any model is a correct representation for a given scenario is a difficult proposition. For example, when using metal powders, the laser powder-bed fusion (L-PBF) process involves complex physical phenomena such as powder morphology, heat transfer, phase transformation, and fluid flow. Models based on these phenomena will possess different degrees of fidelity since they often rely on assumptions that may neglect or simplify process physics, resulting in uncertainties in their prediction accuracy. Prediction accuracy and its characterization can vary greatly between models due to their uncertainties. This paper characterizes several sources of L-PBF model uncertainty for low, medium, and high-fidelity thermal models including modeling assumptions (model-form uncertainty), numerical approximations (numerical uncertainty), and input parameters (parameter uncertainty). This paper focuses on the input uncertainty sources, which we model in terms of a probability density function (PDF), and its propagation through all other L-PBF models. We represent uncertainty sources using the webontologylanguage, which allows us to capture the relevant knowledge used for interoperability and reusability. The topology and mapping of the uncertainty sources establish fundamental requirements for measuring model fidelity and for guiding the selection of a model suitable for its intended purpose.
    Type of Medium: Online Resource
    ISSN: 2332-9017 , 2332-9025
    URL: Article
    DOI: 10.1115/1.4052039
    Language: English
    Publisher: ASME International
    Publication Date: 2022
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Hybrid Modeling Approach for Melt-Pool Prediction in Laser Powder Bed Fusion Additive Manufacturing
    ASME International ; 2021
    In:  Journal of Computing and Information Science in Engineering Vol. 21, No. 5 ( 2021-10-01)
    Details
    In: Journal of Computing and Information Science in Engineering, ASME International, Vol. 21, No. 5 ( 2021-10-01)
    Abstract: Multi-scale, multi-physics, computational models are a promising tool to provide detailed insights to understand the process–structure–property–performance relationships in additive manufacturing (AM) processes. To take advantage of the strengths of both physics-based and data-driven models, we propose a novel, hybrid modeling framework for laser powder bed fusion (L-PBF) process. Our unbiased model-integration method combines physics-based, simulation data, and measurement data for approaching a more accurate prediction of melt-pool width. Both a high-fidelity computational fluid dynamics (CFD) model and experiments utilizing optical images are used to generate a combined dataset of melt-pool widths. From this aggregated data set, a hybrid model is developed using data-driven modeling techniques, including polynomial regression and Kriging methods. The performance of the hybrid model is evaluated by computing the average relative error and comparing it with the results of the simulations and surrogate models constructed from the original CFD model and experimental measurements. It is found that the proposed hybrid model performs better in terms of prediction accuracy and computational time. Future work includes a conceptual introduction to the use of an AM ontology to support improved model and data selection when constructing hybrid models. This study can be viewed as a significant step toward the use of hybrid models as predictive models with improved accuracy and without the sacrifice of speed.
    Type of Medium: Online Resource
    ISSN: 1530-9827 , 1944-7078
    URL: Article
    DOI: 10.1115/1.4050044
    Language: English
    Publisher: ASME International
    Publication Date: 2021
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    Online Resource
  • 3
    Online Resource
    Online Resource
    A Review of Model Inaccuracy and Parameter Uncertainty in Laser Powder Bed Fusion Models and Simulations
    ASME International ; 2019
    In:  Journal of Manufacturing Science and Engineering Vol. 141, No. 4 ( 2019-04-01)
    Details
    In: Journal of Manufacturing Science and Engineering, ASME International, Vol. 141, No. 4 ( 2019-04-01)
    Abstract: This paper presents a comprehensive review on the sources of model inaccuracy and parameter uncertainty in metal laser powder bed fusion (L-PBF) process. Metal additive manufacturing (AM) involves multiple physical phenomena and parameters that potentially affect the quality of the final part. To capture the dynamics and complexity of heat and phase transformations that exist in the metal L-PBF process, computational models and simulations ranging from low to high fidelity have been developed. Since it is difficult to incorporate all the physical phenomena encountered in the L-PBF process, computational models rely on assumptions that may neglect or simplify some physics of the process. Modeling assumptions and uncertainty play significant role in the predictive accuracy of such L-PBF models. In this study, sources of modeling inaccuracy at different stages of the process from powder bed formation to melting and solidification are reviewed. The sources of parameter uncertainty related to material properties and process parameters are also reviewed. The aim of this review is to support the development of an approach to quantify these sources of uncertainty in L-PBF models in the future. The quantification of uncertainty sources is necessary for understanding the tradeoffs in model fidelity and guiding the selection of a model suitable for its intended purpose.
    Type of Medium: Online Resource
    ISSN: 1087-1357 , 1528-8935
    URL: Article
    DOI: 10.1115/1.4042789
    Language: English
    Publisher: ASME International
    Publication Date: 2019
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  • 4
    Online Resource
    Online Resource
    Functional Requirements of Software Tools for Laser-Based Powder Bed Fusion Additive Manufacturing for Metals
    ASME International ; 2023
    In:  Journal of Computing and Information Science in Engineering Vol. 23, No. 3 ( 2023-06-01)
    Details
    In: Journal of Computing and Information Science in Engineering, ASME International, Vol. 23, No. 3 ( 2023-06-01)
    Abstract: Additive manufacturing (AM) for metals is rapidly transitioning to an accepted production technology, which has led to increasing demands for data analysis and software tools. The performance of laser-based powder bed fusion of metals (PBF-LB/M), a common metal AM process, depends on the accuracy of data analysis. Advances in data acquisition and analysis are being propelled by an increase in new types of in situ sensors and ex situ measurement devices. Measurements taken with these sensors and devices rapidly increase the volume, variety, and value of PBF-LB/M data but decrease the veracity of that data simultaneously. The number of new, data-driven software tools capable of analyzing, modeling, simulating, integrating, and managing that data is also increasing; however, the capabilities and accessibility of these tools vary greatly. Issues associated with these software tools are impacting the ability to manage and control PBF-LB/M processes and qualify the resulting parts. This paper investigates and summarizes the available software tools and their capabilities. Findings are then used to help derive a set of functional requirements for tools that are mapped to PBF-LB/M lifecycle activities. The activities include product design, design analysis, process planning, process monitoring, process modeling, process simulation, and production management. PBF-LB/M users can benefit from tools implementing these functional requirements implemented by (1) shortening the lead time of developing these capabilities, (2) adopting emerging, state-of-the-art, PBF-LB/M data and data analytics methods, and (3) enhancing the previously mentioned AM product lifecycle activities.
    Type of Medium: Online Resource
    ISSN: 1530-9827 , 1944-7078
    URL: Article
    DOI: 10.1115/1.4054933
    Language: English
    Publisher: ASME International
    Publication Date: 2023
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    Online Resource
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