scholarly journals Process–Structure–Property Relationships of Copper Parts Manufactured by Laser Powder Bed Fusion

Materials ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 2945
Author(s):  
Mohamed Abdelhafiz ◽  
Kassim S. Al-Rubaie ◽  
Ali Emadi ◽  
Mohamed A. Elbestawi
Keyword(s):  
Laser Power ◽  
Scanning Speed ◽  
Powder Bed Fusion ◽  
Structure Property ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
X Ray ◽  
Structure Property Relationships ◽  
Process Structure ◽  
Hatch Spacing

The process–structure–property relationships of copper laser powder bed fusion (L-PBF)-produced parts made of high purity copper powder (99.9 wt %) are examined in this work. A nominal laser beam diameter of 100 μm with a continuous wavelength of 1080 nm was employed. A wide range of process parameters was considered in this study, including five levels of laser power in the range of 200 to 370 W, nine levels of scanning speed from 200 to 700 mm/s, six levels of hatch spacing from 50 to 150 μm, and two layer thickness values of 30 μm and 40 μm. The influence of preheating was also investigated. A maximum relative density of 96% was obtained at a laser power of 370 W, scanning speed of 500 mm/s, and hatch spacing of 100 μm. The results illustrated the significant influence of some parameters such as laser power and hatch spacing on the part quality. In addition, surface integrity was evaluated by surface roughness measurements, where the optimum Ra was measured at 8 μm ± 0.5 μm. X-ray photoelectron spectroscopy (XPS) and energy-dispersive X-ray spectroscopy (EDX) were performed on the as-built samples to assess the impact of impurities on the L-PBF part characteristics. The highest electrical conductivity recorded for the optimum density-low contaminated coils was 81% IACS.

scholarly journals Laser Powder Bed Fusion of Pure Tungsten: Effects of Process Parameters on Morphology, Densification, Microstructure

Materials ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 165
Author(s):  
Junfeng Li ◽  
Yunxiao Wu ◽  
Bokang Zhou ◽  
Zhengying Wei
Keyword(s):  
Relative Density ◽  
Laser Power ◽  
Grain Morphology ◽  
Scanning Speed ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
High Laser ◽  
Columnar Grains ◽  
Hatch Spacing

Tungsten has been widely used in many industrial fields due to its excellent properties. However, owing to its characteristics of inherent brittleness at room temperature and high melting point, it is difficult to prepare tungsten parts with high complexity via traditional methods. In the present work, tungsten samples were prepared by laser powder bed fusion. The influence of each process parameter including laser power, scanning speed, and hatch spacing on the surface morphology, densification, and microstructure of tungsten samples was systematically investigated. The results showed that the use of the appropriate parameters, especially high laser power, can effectively improve the surface quality and obtain a dense surface. The tungsten samples with a relative density of 98.31% were obtained with optimized parameter combinations: a laser power of 300 W, scanning speed of 400 mm/s, and hatch spacing of 0.08 mm. Compared with scanning speed and hatch spacing, the laser power had a more obvious influence on the relative density. Additionally, for the grain morphology by microstructure inspection, elongated curved grains gradually transformed into fine straight columnar grains as the scanning speed increased. The hatch spacing would change the grain morphology slightly but had no significant effect on the grain size.

scholarly journals Process-structure-property relationships in laser powder bed fusion of permanent magnetic Nd-Fe-B

2021 ◽  
pp. 109992
Author(s):  
Julan Wu ◽  
Nesma T. Aboulkhair ◽  
Michele Degano ◽  
Ian Ashcroft ◽  
Richard J.M. Hague
Keyword(s):  
Powder Bed Fusion ◽  
Structure Property ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Structure Property Relationships ◽  
Process Structure

scholarly journals Knowledge of process-structure-property relationships to engineer better heat treatments for laser powder bed fusion additive manufactured Inconel 718

2020 ◽  
Vol 31 ◽  
pp. 100977 ◽  
Author(s):  
Thomas G. Gallmeyer ◽  
Senthamilaruvi Moorthy ◽  
Branden B. Kappes ◽  
Michael J. Mills ◽  
Behnam Amin-Ahmadi ◽  
...  
Keyword(s):  
Inconel 718 ◽  
Heat Treatments ◽  
Powder Bed Fusion ◽  
Structure Property ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Structure Property Relationships ◽  
Process Structure

scholarly journals In situ microstructure analysis of Inconel 625 during laser powder bed fusion

2021 ◽  
Author(s):  
Felix Schmeiser ◽  
Erwin Krohmer ◽  
Christian Wagner ◽  
Norbert Schell ◽  
Eckart Uhlmann ◽  
...  
Keyword(s):  
Laser Power ◽  
Scanning Speed ◽  
High Energy ◽  
Mechanical Anisotropy ◽  
Ex Situ ◽  
Inconel 625 ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed

AbstractLaser powder bed fusion is an additive manufacturing process that employs highly focused laser radiation for selective melting of a metal powder bed. This process entails a complex heat flow and thermal management that results in characteristic, often highly textured microstructures, which lead to mechanical anisotropy. In this study, high-energy X-ray diffraction experiments were carried out to illuminate the formation and evolution of microstructural features during LPBF. The nickel-base alloy Inconel 625 was used for in situ experiments using a custom LPBF system designed for these investigations. The diffraction patterns yielded results regarding texture, lattice defects, recrystallization, and chemical segregation. A combination of high laser power and scanning speed results in a strong preferred crystallographic orientation, while low laser power and scanning speed showed no clear texture. The observation of a constant gauge volume revealed solid-state texture changes without remelting. They were related to in situ recrystallization processes caused by the repeated laser scanning. After recrystallization, the formation and growth of segregations were deduced from an increasing diffraction peak asymmetry and confirmed by ex situ scanning transmission electron microscopy. Graphical Abstract

Effect of hatch spacing and laser power on microstructure, texture, and thermomechanical properties of laser powder bed fusion (L-PBF) additively manufactured NiTi

2021 ◽  
pp. 107680
Author(s):  
Sayed Ehsan Saghaian ◽  
Mohammadreza Nematollahi ◽  
Guher Toker ◽  
Alejandro Hinojos ◽  
Narges Shayesteh Moghaddam ◽  
...  
Keyword(s):  
Laser Power ◽  
Thermomechanical Properties ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Hatch Spacing

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

2021 ◽  
Author(s):  
Salomé Sanchez ◽  
Divish Rengasamy ◽  
Christopher J. Hyde ◽  
Grazziela P. Figueredo ◽  
Benjamin Rothwell
Keyword(s):  
Creep Rate ◽  
Process Parameters ◽  
Mechanical Performance ◽  
Manufacturing Processes ◽  
Alloy 718 ◽  
Structure Property ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Structure Property Relationships ◽  
Process Structure

AbstractThere 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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