Mitigation of anisotropic fatigue in nickel alloy 718 manufactured via selective laser melting

Sabelkin, V.; Cobb, Gregory R.; Shelton, Travis E.; Hartsfield, Megan N.; Newell, D. J.; O’Hara, Ryan; Kemnitz, Ryan A.

doi:10.1016/j.matdes.2019.108095

Cited by 50 publications

(23 citation statements)

References 17 publications

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“…In the LPBF fabricated alloys, columnar grain texutre usually develops due to the thermal gradient induced during the laser melting process [21,[45][46][47]. Recently, some research studies [28,36,[48][49][50][51] indicate that homogenization at a higher temperature above 1100C could result in the better mechanical performance of Inconel 718, although the phase transformation mechanism behind this is yet unclear. Therefore, it is essential to gain insights into the microstructure evolution during homogenization for microstructure engineering on AM alloys.…”

Section: Introductionmentioning

confidence: 99%

A comparative analysis of Inconel 718 made by additive manufacturing and suction casting: Microstructure evolution in homogenization

Zhao

Gargani

2020

Additive Manufacturing

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Section: Introductionmentioning

confidence: 99%

A comparative analysis of Inconel 718 made by additive manufacturing and suction casting: Microstructure evolution in homogenization

Zhao

Gargani

2020

Additive Manufacturing

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“…Most of these components have been fabricated by using conventional manufacturing approaches such as machining processes, which exhibit significant challenges and difficulties due to the stress and temperature associated with cutting processes and this results in the distortion and dimensional errors of the machined parts [4]. Additive manufacturing has gained rapid interest from the manufacturing industry as it offers great advantages in the manufacturing process due to its innovative approaches and flexibilities such as ability to fabricate complex components with fine details, comparatively less production time and without the use of any kind of tools [5, 6]. Furthermore, additive manufacturing offers high material building efficiency and to a certain extent, superior mechanical properties compared to conventional methods [7, 8].…”

Section: Introductionmentioning

confidence: 99%

Effect of machining and drag finishing on the surface integrity and mechanical properties of Inconel 718 alloys fabricated by laser powder bed fusion additive manufacturing

Karabulut

Kaynak

Sharif

et al. 2022

Materialwissenschaft Werkst

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Laser powder bed fusion process is one of the most widely used Additive Manufacturing techniques for fabricating metal components, however the surface quality of such components still remains a challenge and fails to meet the user expectations. Thus, this study presents a systematic investigation on the effect of post‐processing operations including finish milling and drag finishing on the surface integrity and resulting mechanical properties of Inconel 718 specimens fabricated by laser powder bed fusion additive manufacturing technique. The influence of post‐processing operations on the surface and subsurface microstructure, microhardness and tensile strength on the Inconel 718 specimens are carefully examined and presented in this study. Finally, this work also contributes to understanding the critical wall (part) thickness that is notably induced from the post‐processing operations. It is revealed that the surface integrity and resulting mechanical properties of specimens significantly improved as compared to as‐built specimens once milling and drag finishing post‐processing are deployed. However, the degree of impact depends on the thickness of parts subjected to post‐processing.

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“…The LPBF process results in a specific anisotropic microstructure with elongated columnar grains along the build direction (BD) and equiaxed grains normal to the BD, due to complex cooling gradients present in the part. This results in anisotropic mechanical properties as well (Sabelkin et al 2019). Furthermore, the mechanical performance of LPBF nicke-based superalloys is not yet fully understood and results in lower mechanical performance than conventionally manufactured materials (Xu et al 2018).…”

Section: Introductionmentioning

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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Mitigation of anisotropic fatigue in nickel alloy 718 manufactured via selective laser melting

Cited by 50 publications

References 17 publications

A comparative analysis of Inconel 718 made by additive manufacturing and suction casting: Microstructure evolution in homogenization

A comparative analysis of Inconel 718 made by additive manufacturing and suction casting: Microstructure evolution in homogenization

Effect of machining and drag finishing on the surface integrity and mechanical properties of Inconel 718 alloys fabricated by laser powder bed fusion additive manufacturing

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

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