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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
Controlling the properties of additively manufactured cellular structures using machine learning approaches
Abstract
Cellular structures are lightweight-engineered materials that have gained much attention with the development of additive manufacturing technologies. This paper introduces a precise approach to predict the mechanical properties of additively manufactured lattice structures using deep learning approaches. Diamond shaped nodal lattice structures were designed by varying strut length, strut diameter and strut orientation angle. The samples were manufactured using laser powder bed fusion (LPBF) of Ti-64 alloy and subjected to compression testing to measure the ultimate strength, elastic modulus, and specific strength. Machine learning approaches such as shallow neural network (SNN), deep neural network (DNN), and deep learning neural network (DLNN) were developed and compared to the statistical design of experiment (DoE) approach. The trained DLNN model showed the highest performance when compared to DNN, DoE and SNN with a mean percentage error of 5.26%, 14.60%, and 9.39% for the ultimate strength, elastic modulus, and specific strength, respectively. The DLNN model was used to create process maps, and was further validated. The results showed that although deep learning is preferred for big data, the optimised DLNN model outperformed the statistical DoE approach and can be a favourable tool for lattice structure prediction with limited data.