Selective laser melting of austenitic oxide dispersion strengthened steel: Processing, microstructural evolution and strengthening mechanisms

Ghayoor, Milad; Lee, Kijoon; He, Yujuan; Chang, Chia‐Chin; Paul, Brian K.; Pasebani, Somayeh

doi:10.1016/j.msea.2020.139532

Cited by 63 publications

(26 citation statements)

References 63 publications

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“…Although the strength of the as-printed HEA composite was significantly improved due to the refined microstructures and pinning effects of TiN, the ductility of the alloy was greatly sacrificed. In addition, Zhao et al found that the strength and ductility of the as-printed CoCrFeNiMn HEA were simultaneously enhanced by using a reactive N 2 + Ar atmosphere during L-PBF [92] , as shown in Figure 9A. Figure 9B illustrates the schematic diagram of this L-PBF process.…”

Section: Am Of Precipitation-strengthened Heasmentioning

confidence: 99%

New trends in additive manufacturing of high-entropy alloys and alloy design by machine learning: from single-phase to multiphase systems

Zhou¹,

Zhang²,

Wang³

et al. 2022

J Mater Inf

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Alloys with excellent properties are always in significant demand for meeting the severe conditions of industrial applications. However, the design strategies of traditional alloys based on a single principal element have reached their limits in terms of property optimization. The concept of high-entropy alloys (HEAs) provides a new design strategy based on multicomponent elements, which may overcome the bottleneck problems that exist in traditional alloys. To further maximize the capability of HEAs, a novel additive manufacturing (AM) technique has been utilized to produce HEA components with the desired structures and properties. This review considers a new trend in the AM of HEAs, i.e., from the AM of single-phase HEAs to multiphase HEAs. Although most as-printed single-phase HEAs show superior tensile properties to as-cast ones, their strength is still not satisfactory, especially at elevated temperatures. Thus, multiphase HEAs are developed by introducing hard second phases, such as L12, BCC, carbides, oxides, nitrides, and so on. These phases can be introduced to the matrix using in situ alloying during AM or the subsequent heat treatment. Dislocation strengthening is considered as the main reason for improving the tensile properties of as-printed single-phase HEAs. In contrast, multiple strengthening and toughening mechanisms occur in as-printed multiphase HEAs, which can synergistically enhance their mechanical properties. Furthermore, machine learning provides an effective method to design new alloys with the desired properties and predict the optimal AM parameters for the designed alloys without tedious experiments. The synergistic combination of machine learning and AM will significantly speed up scientific advances and promote industrial applications.

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Section: Am Of Precipitation-strengthened Heasmentioning

confidence: 99%

New trends in additive manufacturing of high-entropy alloys and alloy design by machine learning: from single-phase to multiphase systems

Zhou¹,

Zhang²,

Wang³

et al. 2022

J Mater Inf

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“…[12][13][14][15]20,43 Additionally, LPBF has been applied to austenitic stainless steel powders mechanically alloyed with Y 2 O 3 . [16][17][18][19] While it has not been directly addressed in this study, it would be expected that the general sphericity of powders produced by gas atomization would be well suited for consistent bed formation during LPBF application of ODS powders compared with equivalent MA powders due to the resistance to flow caused by mechanical interlocking between irregular and satellite particles. 54,55,57 The internal structure of the powder particles was examined by TEM to determine the distribution of elements and to confirm the absence of Y-Ti dispersoids in the precursor powder.…”

Section: Powder Characterizationmentioning

confidence: 99%

“…Many studies using laser powder bed fusion (LPBF) AM have utilized ferritic or austenitic steels mechanically alloyed with Y 2 O 3 as AM feedstock. [10][11][12][13][14][15][16][17][18][19][20] Alternatively, low-energy ball milling and blending, [21][22][23] along with methods to deposit oxides onto powders in situ, have been reported as alternative feedstock techniques for fabrication by AM. [24][25][26][27][28] While some promising results have been reported, challenges related to heterogeneity and agglomeration of pre-existing Y 2 O 3 remain, and these approaches typically depend on MA processing, which can be a limiting factor to scalability.…”

Section: Introductionmentioning

confidence: 99%

Laser Powder Bed Fusion of ODS 14YWT from Gas Atomization Reaction Synthesis Precursor Powders

Saptarshi

deJong

Rock

et al. 2022

JOM

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Laser powder bed fusion (LPBF) additive manufacturing (AM) is a promising route for the fabrication of oxide dispersion strengthened (ODS) steels. In this study, 14YWT ferritic steel powders were produced by gas atomization reaction synthesis (GARS). The rapid solidification resulted in the formation of stable, Y-containing intermetallic Y2Fe17 on the interior of the powder and a stable Cr-rich oxide surface. The GARS powders were consolidated with LPBF. Process parameter maps identified a stable process window resulting in a relative density of 99.8%. Transmission electron microscopy and high-energy x-ray diffraction demonstrated that during LPBF, the stable phases in the powder dissociated in the liquid melt pool and reacted to form a high density (1.7 × 1020/m3) of homogeneously distributed Ti2Y2O7 pyrochlore dispersoids ranging from 17 to 57 nm. The use of GARS powder bypasses the mechanical alloying step typically required to produce ODS feedstock. Preliminary mechanical tests demonstrated an ultimate tensile and yield strength of 474 MPa and 312 MPa, respectively.

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“…In addition, the pinning pressure BC on the top-left corner C (C ∈ Γ ) is applied on the hydrodynamic pressure p as p| C = 0 (16) to avoid the difficulties associated with the non-trivial nullspace of the operator prespecified in the PJFNK solver as suggested in Ref. [70].…”

Section: Simulation Setupmentioning

confidence: 99%

“…However, recent studies on this topic show that it is difficult to achieve the optimum nanoparticle size according to the Orowan mechanism due to segregation or agglomeration of the nanoparticles, which in turn was found to deteriorate the mechanical properties of the part [13]. Approaches to optimize the process such as evaluating the influence of the powder characteristics [14] and process parameters [15], or alternative additivation routes such as light mixing [16], improve the dispersion of the nanoparticles; however, they still lack control over the nanoparticle size.…”

Section: Introductionmentioning

confidence: 99%

Nanoparticle Tracing during Laser Powder Bed Fusion of Oxide Dispersion Strengthened Steels

et al. 2021

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The control of nanoparticle agglomeration during the fabrication of oxide dispersion strengthened steels is a key factor in maximizing their mechanical and high temperature reinforcement properties. However, the characterization of the nanoparticle evolution during processing represents a challenge due to the lack of experimental methodologies that allow in situ evaluation during laser powder bed fusion (LPBF) of nanoparticle-additivated steel powders. To address this problem, a simulation scheme is proposed to trace the drift and the interactions of the nanoparticles in the melt pool by joint heat-melt-microstructure–coupled phase-field simulation with nanoparticle kinematics. Van der Waals attraction and electrostatic repulsion with screened-Coulomb potential are explicitly employed to model the interactions with assumptions made based on reported experimental evidence. Numerical simulations have been conducted for LPBF of oxide nanoparticle-additivated PM2000 powder considering various factors, including the nanoparticle composition and size distribution. The obtained results provide a statistical and graphical demonstration of the temporal and spatial variations of the traced nanoparticles, showing ∼55% of the nanoparticles within the generated grains, and a smaller fraction of ∼30% in the pores, ∼13% on the surface, and ∼2% on the grain boundaries. To prove the methodology and compare it with experimental observations, the simulations are performed for LPBF of a 0.005 wt % yttrium oxide nanoparticle-additivated PM2000 powder and the final degree of nanoparticle agglomeration and distribution are analyzed with respect to a series of geometric and material parameters.

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Selective laser melting of austenitic oxide dispersion strengthened steel: Processing, microstructural evolution and strengthening mechanisms

Cited by 63 publications

References 63 publications

New trends in additive manufacturing of high-entropy alloys and alloy design by machine learning: from single-phase to multiphase systems

New trends in additive manufacturing of high-entropy alloys and alloy design by machine learning: from single-phase to multiphase systems

Laser Powder Bed Fusion of ODS 14YWT from Gas Atomization Reaction Synthesis Precursor Powders

Nanoparticle Tracing during Laser Powder Bed Fusion of Oxide Dispersion Strengthened Steels

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