Materials Map

Discover the materials research landscape. Find experts, partners, networks.

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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Materials Map under construction

The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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1.080 Topics available

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977 Locations available

693.932 PEOPLE
693.932 People People

693.932 People

Show results for 693.932 people that are selected by your search filters.

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Naji, M.
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Hassanin, Hany

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Canterbury Christ Church University

in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (19/19 displayed)

  • 2023Hot Air Contactless Single Point Incremental Forming4citations
  • 2022Multipoint Forming Using Hole-Type Rubber Punch2citations
  • 2021Laser powder bed fusion of Ti-6Al-2Sn-4Zr-6Mo alloy and properties prediction using deep learning approaches22citations
  • 2020Controlling the properties of additively manufactured cellular structures using machine learning approaches73citations
  • 20204D printing of origami structures for minimally invasive surgeries using functional scaffold87citations
  • 2018Additive Manufactured Sandwich Composite/ABS Parts for Unmanned Aerial Vehicle Applications76citations
  • 2018Surface finish improvement of additive manufactured metal parts12citations
  • 2018Microfabrication of Net Shape Zirconia/Alumina Nano-Composite Micro Parts13citations
  • 2018Tailoring selective laser melting process for titanium drug-delivering implants with releasing micro-channels71citations
  • 2018Porosity control in 316L stainless steel using cold and hot isostatic pressing66citations
  • 2017Net-Shape Manufacturing using Hybrid Selective Laser Melting/Hot Isostatic Pressing35citations
  • 2017Evolution of grain boundary network topology in 316L austenitic stainless steel during powder hot isostatic pressing54citations
  • 2017Development and Testing of an Additively Manufactured Monolithic Catalyst Bed for HTP Thruster Applications71citations
  • 2016Effect of casting practice on the reliability of Al cast alloys18citations
  • 2016Adding functionality with additive manufacturing : fabrication of titanium-based antibiotic eluting implants78citations
  • 2016Selective Laser Melting of TiNi Auxetic Structurescitations
  • 2016The development of TiNi-based negative Poisson's ratio structure using selective laser melting278citations
  • 2015Influence of processing conditions on strut structure and compressive properties of cellular lattice structures fabricated by selective laser melting321citations
  • 2015In-situ shelling via selective laser melting: modelling and microstructural characterisation35citations

Places of action

Chart of shared publication
Essa, Khamis
13 / 46 shared
Guner, Ahmet
1 / 2 shared
Almadani, Mohammad
1 / 1 shared
Tolipov, Abror
1 / 3 shared
Alfozan, Adel Khalid
1 / 1 shared
Ahmadein, M.
1 / 2 shared
Alsaleh, Naser
1 / 9 shared
Eldessouky, Hossam Mohamed
1 / 1 shared
Zweiri, Yahya
3 / 3 shared
Attallah, Moataz Moataz
10 / 96 shared
Qiu, Chunlei
4 / 14 shared
Finet, Laurane
2 / 2 shared
Alkendi, Yusra
1 / 1 shared
El-Sayed, Mahmoud
3 / 5 shared
Langford, Thomas
1 / 1 shared
Mohammed, Abdullah Hanafi
1 / 1 shared
Elshaer, Amr
3 / 4 shared
Galatas, Athanasios
1 / 1 shared
Seneviratne, Lakmal
1 / 1 shared
Modica, Francesco
1 / 3 shared
Benhadj-Djilali, Redha
1 / 1 shared
Fassi, Irene
1 / 8 shared
Jiang, Kyle
1 / 3 shared
Grover, Liam, M.
1 / 10 shared
Addison, Owen
2 / 43 shared
Shepherd, Duncan Et
1 / 24 shared
Jamshidi, Parastoo
3 / 10 shared
Cox, Sophie C.
2 / 18 shared
Zou, Ji
1 / 12 shared
Hassan, Ali Abdelhafeez
1 / 9 shared
Adkins, Nicholas J. E.
2 / 7 shared
Preuss, Michael
1 / 101 shared
Irukuvarghula, Sandeep
1 / 11 shared
Stewart, David
1 / 9 shared
Cayron, Cyril
1 / 9 shared
Musker, Antony
1 / 1 shared
Roberts, Graham
1 / 2 shared
Smith, Matthew
1 / 9 shared
Adkins, Nicholas
4 / 9 shared
Attallah, Moataz M.
1 / 10 shared
Shepherd, Duncan E. T.
1 / 1 shared
Webber, Mark A.
1 / 2 shared
Eisenstein, Neil M.
1 / 1 shared
Grover, Liam M.
1 / 11 shared
Li, Sheng
2 / 12 shared
Lee, Peter D.
1 / 43 shared
Withers, Philip J.
1 / 38 shared
Yue, Sheng
1 / 2 shared
Ward, Mark
1 / 25 shared
Chart of publication period
2023
2022
2021
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2018
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2015

Co-Authors (by relevance)

  • Essa, Khamis
  • Guner, Ahmet
  • Almadani, Mohammad
  • Tolipov, Abror
  • Alfozan, Adel Khalid
  • Ahmadein, M.
  • Alsaleh, Naser
  • Eldessouky, Hossam Mohamed
  • Zweiri, Yahya
  • Attallah, Moataz Moataz
  • Qiu, Chunlei
  • Finet, Laurane
  • Alkendi, Yusra
  • El-Sayed, Mahmoud
  • Langford, Thomas
  • Mohammed, Abdullah Hanafi
  • Elshaer, Amr
  • Galatas, Athanasios
  • Seneviratne, Lakmal
  • Modica, Francesco
  • Benhadj-Djilali, Redha
  • Fassi, Irene
  • Jiang, Kyle
  • Grover, Liam, M.
  • Addison, Owen
  • Shepherd, Duncan Et
  • Jamshidi, Parastoo
  • Cox, Sophie C.
  • Zou, Ji
  • Hassan, Ali Abdelhafeez
  • Adkins, Nicholas J. E.
  • Preuss, Michael
  • Irukuvarghula, Sandeep
  • Stewart, David
  • Cayron, Cyril
  • Musker, Antony
  • Roberts, Graham
  • Smith, Matthew
  • Adkins, Nicholas
  • Attallah, Moataz M.
  • Shepherd, Duncan E. T.
  • Webber, Mark A.
  • Eisenstein, Neil M.
  • Grover, Liam M.
  • Li, Sheng
  • Lee, Peter D.
  • Withers, Philip J.
  • Yue, Sheng
  • Ward, Mark
OrganizationsLocationPeople

article

Controlling the properties of additively manufactured cellular structures using machine learning approaches

  • Zweiri, Yahya
  • Essa, Khamis
  • Hassanin, Hany
  • Alkendi, Yusra
  • El-Sayed, Mahmoud
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.

Topics
  • impedance spectroscopy
  • experiment
  • strength
  • selective laser melting
  • machine learning