Laser calorimetry for assessment of melting behaviour in multi-walled carbon nanotube decorated aluminium by laser powder bed fusion

CIRP Annals ◽  
2020 ◽  
Vol 69 (1) ◽  
pp. 197-200
Author(s):  
Adam T. Clare ◽  
William J. Reynolds ◽  
James W. Murray ◽  
Nesma T. Aboulkhair ◽  
Marco Simonelli ◽  
...  
Keyword(s):  
Carbon Nanotube ◽  
Powder Bed Fusion ◽  
Melting Behaviour ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Multi Walled Carbon Nanotube ◽  
Laser Calorimetry

scholarly journals Manufacturing Aluminum/Multiwalled Carbon Nanotube Composites via Laser Powder Bed Fusion

Materials ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 3927
Author(s):  
Eo Ryeong Lee ◽  
Se Eun Shin ◽  
Naoki Takata ◽  
Makoto Kobashi ◽  
Masaki Kato
Keyword(s):  
Elastic Modulus ◽  
Carbon Nanotube ◽  
Energy Density ◽  
Laser Power ◽  
Multiwalled Carbon Nanotube ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Multiwalled Carbon ◽  
Powder Bed ◽  
Scan Speed

This study provides a novel approach to fabricating Al/C composites using laser powder bed fusion (LPBF) for a wide range of structural applications utilizing Al-matrix composites in additive manufacturing. We investigated the effects of LPBF on the fabrication of aluminum/multiwalled carbon nanotube (Al/MWCNT) composites under 25 different conditions, using varying laser power levels and scan speeds. The microstructures and mechanical properties of the specimens, such as elastic modulus and nanohardness, were analyzed, and trends were identified. We observed favorable sintering behavior under laser conditions with low energy density, which verified the suitability of Al/MWCNT composites for a fabrication process using LPBF. The size and number of pores increased in specimens produced under high energy density conditions, suggesting that they are more influenced by laser power than scan speed. Similarly, the elastic modulus of a specimen was also more affected by laser power than scan speed. In contrast, scan speed had a greater influence on the final nanohardness. Depending on the laser power used, we observed a difference in the crystallographic orientation of the specimens by a laser power during LPBF. When energy density is high, texture development of all samples tended to be more pronounced.

scholarly journals Machine-learning-assisted microstructure–property linkages of carbon nanotube-reinforced aluminum matrix nanocomposites produced by laser powder bed fusion

2021 ◽  
Vol 10 (1) ◽  
pp. 1410-1424
Author(s):  
Tianyu Yu ◽  
Xuandong Mo ◽  
Mingjun Chen ◽  
Changfeng Yao
Keyword(s):  
Carbon Nanotube ◽  
Regression Models ◽  
Mass Density ◽  
Aluminum Matrix ◽  
Relative Mass ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Density Prediction ◽  
Matrix Nanocomposites

Abstract In this study, the cellular microstructural features in a subgrain size of carbon nanotube (CNT)-reinforced aluminum matrix nanocomposites produced by laser powder bed fusion (LPBF) (a size range between 0.5–1 μm) were quantitatively extracted and calculated from scanning electron microscopy images by applying a cell segmentation method and various image analysis techniques. Over 80 geometric features for each cellular cell were extracted and statistically analyzed using machine learning techniques to explore the structure–property linkages of carbon nanotube reinforced AlSi10Mg nanocomposites. Predictive models for hardness and relative mass density were established using these subgrain cellular microstructural features. Data dimension reduction using principal component analysis was conducted to reduce the feature number to 3. The results showed that even AlSi10Mg nanocomposite specimens produced using different laser parameters exhibited similar Al–Si eutectic microstructures, displaying a large difference in their mechanical properties including hardness and relative mass density due to cellular structure variance. For hardness prediction, the Extra Tress regression models showed a relative error of 2.47% for prediction accuracies. For the relative mass density prediction, the Decision Tress regression models showed a relative error of 1.42% for prediction accuracies. The results demonstrate that the developed models deliver satisfactory performance for hardness and relative mass density prediction of AlSi10Mg nanocomposites. The framework established in this study can be applied to the LPBF process optimization and mechanical properties manipulation of AlSi10Mg-based alloys and other additive manufacturing newly designed alloys or composites.

scholarly journals Effect of shielding conditions on bead profile and melting behaviour in laser powder bed fusion additive manufacturing

2020 ◽  
Vol 34 ◽  
pp. 101342 ◽  
Author(s):  
Armando Caballero ◽  
Wojciech Suder ◽  
Xin Chen ◽  
Goncalo Pardal ◽  
Stewart Williams
Keyword(s):  
Additive Manufacturing ◽  
Powder Bed Fusion ◽  
Melting Behaviour ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Bead Profile

scholarly journals On the Relevance of Volumetric Energy Density in the Investigation of Inconel 718 Laser Powder Bed Fusion

Materials ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 538 ◽  
Author(s):  
Fabrizia Caiazzo ◽  
Vittorio Alfieri ◽  
Giuseppe Casalino
Keyword(s):  
Surface Roughness ◽  
Energy Density ◽  
Process Parameters ◽  
Vickers Hardness ◽  
Powder Bed Fusion ◽  
Processing Conditions ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Fractional Density ◽  
Experimental Variation

Laser powder bed fusion (LPBF) can fabricate products with tailored mechanical and surface properties. In fact, surface texture, roughness, pore size, the resulting fractional density, and microhardness highly depend on the processing conditions, which are very difficult to deal with. Therefore, this paper aims at investigating the relevance of the volumetric energy density (VED) that is a concise index of some governing factors with a potential operational use. This paper proves the fact that the observed experimental variation in the surface roughness, number and size of pores, the fractional density, and Vickers hardness can be explained in terms of VED that can help the investigator in dealing with several process parameters at once.

Heat source model calibration for thermal analysis of laser powder-bed fusion

2020 ◽  
Vol 106 (7-8) ◽  
pp. 3367-3379 ◽  
Author(s):  
Shahriar Imani Shahabad ◽  
Zhidong Zhang ◽  
Ali Keshavarzkermani ◽  
Usman Ali ◽  
Yahya Mahmoodkhani ◽  
...  
Keyword(s):  
Thermal Analysis ◽  
Heat Source ◽  
Model Calibration ◽  
Source Model ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Heat Source Model ◽  
Powder Bed

scholarly journals The Effect of Cu Content and Surface Finish on the Metal Dusting Resistance of Additively Manufactured NiCu Alloys

2021 ◽  
Author(s):  
Katrin Jahns ◽  
Anke S. Ulrich ◽  
Clara Schlereth ◽  
Lukas Reiff ◽  
Ulrich Krupp ◽  
...  
Keyword(s):  
Surface Finish ◽  
Powder Bed Fusion ◽  
Metal Dusting ◽  
Laser Powder Bed Fusion ◽  
Roughness Measurement ◽  
Powder Bed ◽  
Cu Content ◽  
Binary Model ◽  
Interesting Candidate ◽  
Nicu Alloys

AbstractDue to the inhibiting behavior of Cu, NiCu alloys represent an interesting candidate in carburizing atmospheres. However, manufacturing by conventional casting is limited. It is important to know whether the corrosion behavior of conventionally and additively manufactured parts differ. Samples of binary NiCu alloys and Monel Alloy 400 were generated by laser powder bed fusion (LPBF) and exposed to a carburizing atmosphere (20 vol% CO–20% H2–1% H2O–8% CO2–51% Ar) at 620 °C and 18 bar for 960 h. Powders and printed samples were investigated using several analytic techniques such as EPMA, SEM, and roughness measurement. Grinding of the material after building (P1200 grit surface finish) generally reduced the metal dusting attack. Comparing the different compositions, a much lower attack was found in the case of the binary model alloys, whereas the technical Monel Alloy 400 showed a four orders of magnitude higher mass loss during exposure despite its Cu content of more than 30 wt%.

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