scholarly journals Research on Selective Laser Melting of Ti6Al4V: Surface Morphologies, Optimized Processing Zone, and Ductility Improvement Mechanism

Metals ◽  
2018 ◽  
Vol 8 (7) ◽  
pp. 471 ◽  
Author(s):  
Di Wang ◽  
Wenhao Dou ◽  
Yongqiang Yang
Keyword(s):  
Selective Laser Melting ◽  
Laser Melting ◽  
Processing Zone ◽  
Surface Morphologies

scholarly journals Effect of Alloying Mn by Selective Laser Melting on the Microstructure and Biodegradation Properties of Pure Mg

Metals ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 1527
Author(s):  
Bin Xie ◽  
Ming-Chun Zhao ◽  
Ying-Chao Zhao ◽  
Yan Tian ◽  
Dengfeng Yin ◽  
...  
Keyword(s):  
Manganese Oxide ◽  
Selective Laser Melting ◽  
Immersion Test ◽  
Biodegradation Rate ◽  
Laser Melting ◽  
Xps Spectra ◽  
Average Grain Size ◽  
Mn Content ◽  
Surface Morphologies ◽  
Effect Of Alloying

This work studied the effect of alloying Mn by selective laser melting on the microstructure and biodegradation properties of pure Mg. The grains in the microstructure were quasi-polygon in shape. The average grain size was similar (~10 μm) for the SLMed Mg-xMn with different Mn contents. The XPS spectra of the corrosion surface showed that alloying Mn into Mg by SLM produced a relatively protective manganese oxide film, which contributed to decreasing the biodegradation rate. All the results of the electrochemistry test, immersion test and the corrosion surface morphologies coincided well. The SLMed Mg-0.8Mn had the lowest biodegradation rate. When Mn content was more than 0.8 wt.%, the influence of the undissolved Mn phase on the decrease of the biodegradation resistance counteracted the influence of the relatively protective manganese oxide layer on the increase of the biodegradation resistance.

scholarly journals Interferometry of Gas-Phase Flows during Selective Laser Melting

2019 ◽  
Vol 10 (1) ◽  
pp. 231 ◽  
Author(s):  
Pavel A. Podrabinnik ◽  
Alexander E. Shtanko ◽  
Roman S. Khmyrov ◽  
Andrey D. Korotkov ◽  
Andrey V. Gusarov
Keyword(s):  
Gas Phase ◽  
Selective Laser Melting ◽  
Laser Power ◽  
Metal Vapor ◽  
Gas Jet ◽  
Laser Melting ◽  
Pressure Fields ◽  
Ambient Gas ◽  
Processing Zone

Gas-phase flows occurring in a plume in a processing zone during selective laser melting (SLM) can significantly affect the quality of the process. To further enhance SLM performance, the characteristics of the flows should be considered. In this article, the vapor-gas jet emerging from the laser processing zone was studied. It was visualized by interferometry to evaluate flow velocity, geometry and changes in refractory index depending on laser power. The velocity and pressure fields of the vapor jet and the entrained ambient gas were estimated by mathematical modeling. It was shown that the increase of laser power led to higher jet velocity and greater change in its refractory index. The latter also was used to evaluate the content of metal vapor in the plume and its influence on the absorption of laser radiation.

scholarly journals As-fabricated surface morphologies of Ti-6Al-4V samples fabricated by different laser processing parameters in selective laser melting

2020 ◽  
Vol 33 ◽  
pp. 101147 ◽  
Author(s):  
Snehashis Pal ◽  
Gorazd Lojen ◽  
Radovan Hudak ◽  
Viktoria Rajtukova ◽  
Tomaž Brajlih ◽  
...  
Keyword(s):  
Selective Laser Melting ◽  
Laser Processing ◽  
Processing Parameters ◽  
Laser Melting ◽  
Surface Morphologies

scholarly journals Predicting the Printability in Selective Laser Melting with a Supervised Machine Learning Method

Materials ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 5063
Author(s):  
Yingyan Chen ◽  
Hongze Wang ◽  
Yi Wu ◽  
Haowei Wang
Keyword(s):  
Machine Learning ◽  
Selective Laser Melting ◽  
Process Parameter ◽  
Supervised Machine Learning ◽  
Laser Melting ◽  
Metallographic Examination ◽  
Free Process ◽  
Input Variables ◽  
Surface Morphologies

Though selective laser melting (SLM) has a rapidly increasing market these years, the quality of the SLM-fabricated part is extremely dependent on the process parameters. However, the current metallographic examination method to find the parameter window is time-consuming and involves subjective assessments of the experimenters. Here, we proposed a supervised machine learning (ML) method to detect the track defect and predict the printability of material in SLM intelligently. The printed tracks were classified into five types based on the measured surface morphologies and characteristics. The classification results were used as the target output of the ML model. Four indicators had been calculated to evaluate the quality of the tracks quantitatively, serving as input variables of the model. The data-driven model can determine the defect-free process parameter combination, which significantly improves the efficiency in searching the process parameter window and has great potential for the application in the unmanned factory in the future.

MANUFACTURING NOVEL HEAT TRANSFER DEVICES BY SELECTIVE LASER MELTING

Equipment ◽  
2006 ◽  
Author(s):  
S. Tsopanos ◽  
M. Wong ◽  
I. Owen ◽  
C. J. Sutcliffe
Keyword(s):  
Heat Transfer ◽  
Selective Laser Melting ◽  
Laser Melting

Properties of Ti-6Al-4V titanium alloys obtained by selective laser melting

2019 ◽  
pp. 46-57
Author(s):  
M.A. Kaplan ◽  
◽  
М.A. Smirnov ◽  
A.A. Kirsankin ◽  
M.A. Sevostyanov ◽  
...  
Keyword(s):  
Titanium Alloys ◽  
Selective Laser Melting ◽  
Laser Melting
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