Optical response of nickel-based superalloy Inconel-718 for applications in additive manufacturing

Curry, Erin; Sahoo, Sanjubala; Herrera, Chloe; Sochnikov, Ilya; Alpay, S. P.; Hebert, Rainer J.; Willis, Brian G.; Qi, Jie; Hancock, Jason

doi:10.1063/5.0006006

Cited by 12 publications

(4 citation statements)

References 29 publications

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“…However, to the best of the authors' knowledge, these optical properties are not reported in the literature. Consequently, in accordance with the experimental data provided in Figure 2(a) of (Curry et al, 2020), the real and imaginary parts of the dielectric function, e 1 and e 2 , corresponding to a Nd:YAG laser with a wavelength of 1,064 nm were extracted as À18.6 and 39.7, respectively. Accordingly, the ordinary refractive index and extinction coefficient of IN718 was estimated as (Fox, 2002).…”

Section: Powder Bed and Monte Carlo Ray-tracing Simulationsupporting

confidence: 59%

“…New features of the proposed framework in comparison with previous works (Bajaj et al, 2019;Yadroitsev et al, 2015;Kamath, 2016;Mirkoohi et al, 2018;Boley et al, 2015) are briefly introduced as follows. The refractive index of IN718 material is not available, thus the measurement results of dielectric functions attained from literature (Curry et al, 2020) were used to calculate the refractive index of this material in the micro-scale model. Additionally, the processing maps of laser power and scanning speed corresponding to the different temperature of the solidified layer beneath the powder bed is constructed.…”

Section: Introductionmentioning

confidence: 99%

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Multi-scale simulation approach for identifying optimal parameters for fabrication ofhigh-density Inconel 718 parts using selective laser melting

Tran

et al. 2021

RPJ

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Purpose Depending on an experimental approach to find optimal parameters for producing fully dense (relative density > 99%) Inconel 718 (IN718) components in the selective laser melting (SLM) process is expensive and offers no guarantee of success. Accordingly, this study aims to propose a multi-scale simulation framework to guide the choice of processing parameters in a more pragmatic manner. Design/methodology/approach In the proposed approach, a powder layer, ray tracing and heat transfer simulation models are used to calculate the melt pool dimensions and evaporation volume corresponding to a small number of laser power and scanning speed conditions within the input design space. A layer-heating model is then used to determine the inter-layer idle time required to maximize the temperature convergence rate of the solidified layer beneath the power bed. The simulation results are used to train surrogate models to construct SLM process maps for 3,600 pairs of the laser power and scanning speed within the input design space given three different values of the underlying solidified layer temperature (i.e., 353 K, 673 K and 873 K). The ideal selection of laser power and scanning speed of each process map is chosen based on four quality-related criteria listed as follows: without the appearance of key-hole melting; an evaporation volume less than the volume of the d90 powder particles; ensuring the stability of single scan tracks; and avoiding a weak contact between the melt pool and substrate. Finally, the optimal laser power and scanning speed parameters for the SLM process are determined by superimposing the optimal regions of the individual process maps. Findings The feasibility of the proposed approach is demonstrated by fabricating IN718 test specimens using the optimal processing conditions identified by the simulation framework. It is shown that the maximum density of the fabricated parts is 99.94%, while the average density is 99.88% and the standard deviation is less than 0.05%. Originality/value The present study proposed a multi-scale simulation model which can efficiently predict the optimal processing conditions for producing fully dense components in the SLM process. If the geometry of the three-dimensional printed part is changed or the machine and powder material is altered, users can use the proposed method for predicting the processing conditions that can produce the high-density part.

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Section: Powder Bed and Monte Carlo Ray-tracing Simulationsupporting

confidence: 59%

Section: Introductionmentioning

confidence: 99%

Multi-scale simulation approach for identifying optimal parameters for fabrication ofhigh-density Inconel 718 parts using selective laser melting

Tran

et al. 2021

RPJ

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“…The accuracy of DFT for materials properties that are determined at the electronic and atomic levels has been well tested; it provides a platform to bridge the gap between fundamental theory, high-throughput computational data science, and experiments [29][30][31][32][33][34][35][36][37] . Here, we perform first-principles calculations with DFT using the plane-wave pseudopotential method 38 .…”

Section: First Principles (Dft) Validationmentioning

confidence: 99%

Data-driven methods for discovery of next-generation electrostrictive materials

Trujillo

Gurung

et al. 2022

npj Comput Mater

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All dielectrics exhibit electrostriction, i.e., display a quadratic strain response to an electric field compared to the linear strain dependence of piezoelectrics. As such, there is significant interest in discovering new electrostrictors with enhanced electrostrictive coefficients, especially as electrostrictors can exhibit effective piezoelectricity when a bias electric field is applied. We present the results of a study combining data mining and first-principles computations that indicate that there exists a group of iodides, bromides, and chlorides that have electrostrictive coefficients exceeding 10 m4 C–2 which are substantially higher than typical oxide electrostrictive ceramics and polymers. The corresponding effective piezoelectric voltage coefficients are three orders of magnitude larger than lead zirconate titanate.

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“…[41] A recent study focused on radiative cooling in DED-AM and obtained the dielectric constant of the nickel-based superalloy Inconel-718 via experiments as well as DFT calculations to determine the spectral reflectivity and emissivity. [42] Due to the generality of the DFT calculation, these reported methods can be applied to a large variety of materials and can be readily turned into tools for materials design and quality control for AM.…”

Section: Dft and High-throughput Dftmentioning

confidence: 99%

Data‐Driven Approaches Toward Smarter Additive Manufacturing

Tian

Bustillos

et al. 2021

Advanced Intelligent Systems

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The latest industrial revolution, Industry 4.0, is driven by the emergence of digital manufacturing and, most notably, additive manufacturing (AM) technologies. The simultaneous material and structure forming in AM broadens the material and structural design space. This expanded design space holds a great potential in creating improved engineering materials and products that attract growing interests from both academia and industry. A major aspect of this growing interest is reflected in the increased adaptation of data‐driven tools that accelerate the exploration of the vast design space in AM. Herein, the integration of data‐driven tools in various aspects of AM is reviewed, from materials design in AM (i.e., homogeneous and composite material design) to structure design for AM (i.e., topology optimization). The optimization of AM tool path using machine learning for producing best‐quality AM products with optimal material and structure is also discussed. Finally, the perspectives on the future development of holistically integrated frameworks of AM and data‐driven methods are provided.

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Optical response of nickel-based superalloy Inconel-718 for applications in additive manufacturing

Cited by 12 publications

References 29 publications

Multi-scale simulation approach for identifying optimal parameters for fabrication ofhigh-density Inconel 718 parts using selective laser melting

Multi-scale simulation approach for identifying optimal parameters for fabrication ofhigh-density Inconel 718 parts using selective laser melting

Data-driven methods for discovery of next-generation electrostrictive materials

Data‐Driven Approaches Toward Smarter Additive Manufacturing

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