Additive Manufacturing of Functional Inconel 718 Parts from Recycled Materials

Tupac-Yupanqui, Hugo; Armani, Amir

doi:10.1007/s11665-020-05402-8

Cited by 9 publications

(3 citation statements)

References 17 publications

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“…The heat transfer analysis provides the transient temperature field that is transferred to the mechanical analysis model [43]. The same workbench additive model has been used in many similar studies [44][45][46] to validate the code and to optimize the SLM build part. To set up an AM simulation in Ansys, we can either create our geometry in the software or we can import the stl.…”

Section: Application To An Additive Manufacturing Benchmark Testmentioning

confidence: 99%

A Comparative Study of Machine Learning Methods for Computational Modeling of the Selective Laser Melting Additive Manufacturing Process

Chaudhry

Soulaïmani

2022

Applied Sciences

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Selective laser melting (SLM) is a metal-based additive manufacturing (AM) technique. Many factors contribute to the output quality of SLM, particularly the machine and material parameters. Analysis of the parameters’ effects is critical, but using traditional experimental and numerical simulation can be expensive and time-consuming. This paper provides a framework to analyze the sensitivity and uncertainty in SLM input and output parameters, which can then be used to find the optimum parameters. The proposed data-driven approach combines machine learning algorithms with high-fidelity numerical simulations to study the SLM process more efficiently. We have considered laser speed, hatch spacing, layer thickness, Young modulus, and Poisson ratio as input variables, while the output variables are numerical predicted normal strains in the building part. A surrogate model was constructed with a deep neural network (DNN) or polynomial chaos expansion (PCE) to generate a response surface between the SLM output and the input variables. The surrogate model and the sensitivity analysis found that all five parameters were important in the process. The surrogate model was combined with non-intrusive optimization algorithms such as genetic algorithms (GA), differential evolution (DE), and particle swarm optimization (PSO) to perform an inverse analysis and find the optimal parameters for the SLM process. Of the three algorithms, the PSO performed well, and the DNN model was found to be the most efficient surrogate model compared to the PCE.

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Section: Application To An Additive Manufacturing Benchmark Testmentioning

confidence: 99%

A Comparative Study of Machine Learning Methods for Computational Modeling of the Selective Laser Melting Additive Manufacturing Process

Chaudhry

Soulaïmani

2022

Applied Sciences

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“…Numerous studies underscore the successful application of LPBF in printing open impellers, encompassing investigations into material properties, thermal history predictions, and optimization of process parameters [14][15][16]. Significantly, LPBF allows for the fabrication of impellers from recycled materials, accentuating its potential for sustainable manufacturing practices [17]. Despite these advancements, persistent challenges revolve around achieving precise and defect-free 3D prints.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Manufacturing 316L Stainless Steel Closed Impeller Using Laser Powder Bed Fusion

Al Horr,

Alkhalaf

2024

Mater. Plus

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The manufacturing of closed impellers via Laser Powder Bed Fusion technology entails inherent complexities, demanding meticulous attention to support structure design, determination of building angle, and down-skin energy density to ensure successful production. This study explores the use of stainless steel 316L in fabricating closed impellers using LPBF. The aim is to investigate the impact of process parameters and building angles on impeller fabrication quality. In addition to studying the effect of energy density on the surface roughness and hardness values of the impeller. The investigation examines building angles of 0˚, 30˚, and 90˚ for a closed impeller, revealing that a 30˚ building angle yields successful printed parts. Moreover, to achieve defect-free closed impellers, it is imperative to maintain down-skin energy density between 80% and 100% of in-skin energy density. The maximum hardness and minimum surface roughness were recorded at 233 HV and 12.79 µm, respectively, when the energy density was 41.66 J/mm³ and 55.55 J/mm³ respectively. The novelty of this study lies in the fabrication of closed impellers using stainless steel 316L through laser powder bed fusion.

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“…Yupanqui, Christopher Kantzos and B.C. Brennan [4][5][6] analyzed effect of a variety of processing parameters on melt-pool geometry and microstructure development of Inconel 718 to reduce metallurgical defects during fusion-based laser powder bed additive manufacture processes. G.E.…”

Section: Introductionmentioning

confidence: 99%

Numerical Analysis of Nucleation and Growth of Stray Grain Formation during Laser Welding Nickel-Based Single-Crystal Superalloy Part III: Kinetic and Thermodynamic Obviation of Solidification Cracking

Gao

2022

SSP

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To pertinently balance growth kinetics, solidification thermodynamics and dendrite expitaxy of multicomponent nickel-based single-crystal supralloy during laser processing, effect of thermometallurgy determinant factors, including laser power, welding speed and welding configuration, on solidification behavior, such as nonequilibrium solidification temperature range, and dendrite growth, such as dendrite trunk spacing, are progressively advanced to forestall solidification cracking phenomena. Symmetric developments of dendrite trunk spacing and solidification temperature range alongside solid/liquid interface are crystallographically driven by useful (001)/[100] welding configuration to auspiciously bring about crack-insusceptible and well-oriented dendrite growth. Dissimilarly, unsymmetrical developments of dendrite trunk spacing and solidification temperature range alongside solid/liquid interface are crystallographically driven by (001)/[110] welding configuration to insidiously favor crack-unresistant and disoriented dendrite growth. Higher heat input thermodynamically and kinetically boosts wide solidification temperature range, appalling stray grain growth with excess of solute ahead of dendrite tip and large size of crack-unresistant region to thermometallurgically disintegrate epitaxial growth for untoward solidification cracking, and therefore should be strictly withstood. Although geometry of symmetrical weld pool both sides is the same in infelicitous (001)/[110] welding configuration, [100] region of dendrite growth is more liable to ruinous stray grain growth and extensive solidification temperature range than [010] region of dendrite growth to complicate dendrite growth and exacerbate weld integrity. The determinant mechanism of crystallography-aided amelioration of solidification cracking resistance as result of kinetics-and thermodynamics-driven dendrite growth is propitiously proposed. Furthermore, the credible and understandable theoretical predictions are in conformity with the experiment results.

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Additive Manufacturing of Functional Inconel 718 Parts from Recycled Materials

Cited by 9 publications

References 17 publications

A Comparative Study of Machine Learning Methods for Computational Modeling of the Selective Laser Melting Additive Manufacturing Process

A Comparative Study of Machine Learning Methods for Computational Modeling of the Selective Laser Melting Additive Manufacturing Process

Investigation of Manufacturing 316L Stainless Steel Closed Impeller Using Laser Powder Bed Fusion

Numerical Analysis of Nucleation and Growth of Stray Grain Formation during Laser Welding Nickel-Based Single-Crystal Superalloy Part III: Kinetic and Thermodynamic Obviation of Solidification Cracking

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