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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Hassanin, Hany
Canterbury Christ Church University
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (19/19 displayed)
- 2023Hot Air Contactless Single Point Incremental Formingcitations
- 2022Multipoint Forming Using Hole-Type Rubber Punchcitations
- 2021Laser powder bed fusion of Ti-6Al-2Sn-4Zr-6Mo alloy and properties prediction using deep learning approachescitations
- 2020Controlling the properties of additively manufactured cellular structures using machine learning approachescitations
- 20204D printing of origami structures for minimally invasive surgeries using functional scaffoldcitations
- 2018Additive Manufactured Sandwich Composite/ABS Parts for Unmanned Aerial Vehicle Applicationscitations
- 2018Surface finish improvement of additive manufactured metal partscitations
- 2018Microfabrication of Net Shape Zirconia/Alumina Nano-Composite Micro Partscitations
- 2018Tailoring selective laser melting process for titanium drug-delivering implants with releasing micro-channelscitations
- 2018Porosity control in 316L stainless steel using cold and hot isostatic pressingcitations
- 2017Net-Shape Manufacturing using Hybrid Selective Laser Melting/Hot Isostatic Pressingcitations
- 2017Evolution of grain boundary network topology in 316L austenitic stainless steel during powder hot isostatic pressingcitations
- 2017Development and Testing of an Additively Manufactured Monolithic Catalyst Bed for HTP Thruster Applicationscitations
- 2016Effect of casting practice on the reliability of Al cast alloyscitations
- 2016Adding functionality with additive manufacturing : fabrication of titanium-based antibiotic eluting implantscitations
- 2016Selective Laser Melting of TiNi Auxetic Structures
- 2016The development of TiNi-based negative Poisson's ratio structure using selective laser meltingcitations
- 2015Influence of processing conditions on strut structure and compressive properties of cellular lattice structures fabricated by selective laser meltingcitations
- 2015In-situ shelling via selective laser melting: modelling and microstructural characterisationcitations
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article
Laser powder bed fusion of Ti-6Al-2Sn-4Zr-6Mo alloy and properties prediction using deep learning approaches
Abstract
<p>Ti-6Al-2Sn-4Zr-6Mo is one of the most important titanium alloys characterised by its high strength, fatigue, and toughness properties, making it a popular material for aerospace and biomedical applications. However, no studies have been reported on processing this alloy using laser powder bed fusion. In this paper, a deep learning neural network (DLNN) was introduced to rationalise and predict the densification and hardness due to Laser Powder Bed Fusion of Ti-6Al-2Sn-4Zr-6Mo alloy. The process optimisation results showed that near-full densification is achieved in Ti-6Al-2Sn-4Zr-6Mo alloy samples fabricated using an energy density of 77-113 J/mm<sup>3</sup>. Furthermore, the hardness of the builds was found to increase with increasing the laser energy density. Porosity and the hardness measurements were found to be sensitive to the island size, especially at high energy density. Hot isostatic pressing (HIP) was able to eliminate the porosity, increase the hardness, and achieve the desirable α and β phases. The developed model was validated and used to produce process maps. The trained deep learning neural network model showed the highest accuracy with a mean percentage error of 3% and 0.2% for the porosity and hardness. The results showed that deep learning neural networks could be an efficient tool for predicting materials properties using small data. </p>