In-Situ Monitoring for Defect Identification in Nickel Alloy Complex Geometries Fabricated by L-PBF Additive Manufacturing

McNeil, J. Logan; Sisco, Kevin; Frederick, Curt; Massey, M. E.; Carver, Keith; List, F.A.; Qiu, Caian; Mader, Morgan C.; Sundarraj, Suresh; Babu, S. S.

doi:10.1007/s11661-020-06036-0

Cited by 25 publications

(7 citation statements)

References 44 publications

(62 reference statements)

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“…Furthermore, the porosity analysis was performed using cubes, while the creep specimen were cylinders. Some studies on Electron Beam Powder Bed Fusion of various metals have found that the sample geometry affects the porosity (Frederick et al 2018;McNeil et al 2020), pore location (Yoder et al 2019) and mechanical properties (Yoder et al 2018). However, these studies compared simple (e.g.…”

Section: Effect On Densitymentioning

confidence: 99%

Machine learning to determine the main factors affecting creep rates in laser powder bed fusion

et al. 2021

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There is an increasing need for the use of additive manufacturing (AM) to produce improved critical application engineering components. However, the materials manufactured using AM perform well below their traditionally manufactured counterparts, particularly for creep and fatigue. Research has shown that this difference in performance is due to the complex relationships between AM process parameters which affect the material microstructure and consequently the mechanical performance as well. Therefore, it is necessary to understand the impact of different AM build parameters on the mechanical performance of parts. Machine learning (ML) models are able to find hidden relationships in data using iterative statistical analyses and have the potential to develop process–structure–property–performance relationships for manufacturing processes, including AM. The aim of this work is to apply ML techniques to materials testing data in order to understand the effect of AM process parameters on the creep rate of additively built nickel-based superalloy and to predict the creep rate of the material from these process parameters. In this work, the predictive capabilities of ML and its ability to develop process–structure–property relationships are applied to the creep properties of laser powder bed fused alloy 718. The input data for the ML model included the Laser Powder Bed Fusion (LPBF) build parameters used—build orientation, scan strategy and number of lasers—and geometrical material descriptors which were extracted from optical microscope porosity images using image analysis techniques. The ML model was used to predict the minimum creep rate of the Laser Powder Bed Fused alloy 718 samples, which had been creep tested at $$650\,^\circ $$ 650 ∘ C and 600 MPa. The ML model was also used to identify the most relevant material descriptors affecting the minimum creep rate of the material (determined by using an ensemble feature importance framework). The creep rate was accurately predicted with a percentage error of $$1.40\%$$ 1.40 % in the best case. The most important material descriptors were found to be part density, number of pores, build orientation and scan strategy. These findings show the applicability and potential of using ML to determine and predict the mechanical properties of materials fabricated via different manufacturing processes, and to find process–structure–property relationships in AM. This increases the readiness of AM for use in critical applications.

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Section: Effect On Densitymentioning

confidence: 99%

Machine learning to determine the main factors affecting creep rates in laser powder bed fusion

et al. 2021

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“…The ability to scan objects in 3D without slicing or sectioning is the biggest advantage of X-ray CT over other microscopy techniques, such as scanning electron microscopy, transmission electron microscopy and optical microscopy. X-ray CT has been used to study various 3D printed materials in terms of size, spatial distribution and the morphology of defects [ 2 , 3 , 4 , 5 , 6 , 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

X-ray Tomographic Method to Study the Internal Structure of a TiNi–TiB2 Metal Matrix Composite Obtained by Direct Laser Deposition

et al. 2023

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The field of additive manufacturing (AM) of various materials is rapidly developing. At the stage of designing and growing products and for the quality control of finished parts, non-destructive methods of analysis, in particular X-ray computed tomography (CT), are in demand. In addition to the advantages of non-destructive imaging of a wide range of materials in three dimensions, modern CT scanners offer a high contrast and high spatial resolution to provide digital information about their three-dimensional geometry and properties. Within the framework of this article, CT was used to follow the structural evolution of a TiNi–TiB2 metal–ceramic composite obtained by direct laser deposition. The relationship has been established between the additive method of production (layered direct laser deposition) and the formed layered structure of the product in the direction of growth. The porosity of the sample was calculated for each scan direction, and the average for the sample was 1.96%. The matrix of the TiNi–TiB2 composite is characterized by the presence of pores of various sizes, shapes and locations. Spherical voids prevail, but keyhole pores are also found. The heterogeneity of the structure was revealed in the form of clearly traced boundaries of the print layers, as well as differences in the density of the inner and outer regions of the composite.

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“…[13][14][15] Moreover, recent advances have substantially supported the use of various wire materials such as aluminum, [16][17][18] titanium, [19][20][21] and nickel alloys. [22][23][24][25] However, the path generation in the arc-based metal AM is still based on a point-by-point or layer-by-layer manufacturing approach. [26][27][28] Thus, their print speed is limited by stepwise material deposition due to the time of solidification and cooling of molten metals during the printing process.…”

Section: Introductionmentioning

confidence: 99%

High‐Throughput Metal 3D Printing Pen Enabled by a Continuous Molten Droplet Transfer

et al. 2022

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In metal additive manufacturing (AM), arc plasma is attracting attention as an alternative heat source to expensive lasers to enable the use of various metal wire materials with a high deposition efficiency. However, the stepwise material deposition and resulting limited number of degrees of freedom limit their potential for high-throughput and large-scale production for industrial applications. Herein, a high-throughput metal 3D printing pen (M3DPen) strategy is proposed based on an arc plasma heat source by harnessing the surface tension of the molten metal for enabling continuous material deposition without a downward flow by gravity. The proposed approach differs from conventional arc-based metal AM in that it controls the solidification and cooling time between interlayers of a point-by-point deposition path, thereby allowing for continuous metal 3D printing of freestanding and overhanging structures at once. The resulting mechanical properties and unique microstructures by continuous metal deposition that occur due to the difference in the thermal conditions of the molten metal under cooling are also investigated. This technology can be applied to a wide range of alloy systems and industrial manufacturing, thereby providing new possibilities for metal 3D printing.

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In-Situ Monitoring for Defect Identification in Nickel Alloy Complex Geometries Fabricated by L-PBF Additive Manufacturing

Cited by 25 publications

References 44 publications

Machine learning to determine the main factors affecting creep rates in laser powder bed fusion

Machine learning to determine the main factors affecting creep rates in laser powder bed fusion

X-ray Tomographic Method to Study the Internal Structure of a TiNi–TiB2 Metal Matrix Composite Obtained by Direct Laser Deposition

High‐Throughput Metal 3D Printing Pen Enabled by a Continuous Molten Droplet Transfer

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