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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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Reis, A.
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (20/20 displayed)
- 2023An ethics framework for social listening and infodemic managementcitations
- 2023A Review on Direct Laser Deposition of Inconel 625 and Inconel 625-Based Composites-Challenges and Prospectscitations
- 2023Adding Value to Secondary Aluminum Casting Alloys: A Review on Trends and Achievementscitations
- 2023A Predictive Methodology for Temperature, Heat Generation and Transfer in Gigacycle Fatigue Testingcitations
- 2023Infiltration of aluminum in 3D-printed metallic inserts
- 2022Finite Element Analysis of Distortions, Residual Stresses and Residual Strains in Laser Powder Bed Fusion-Produced Components
- 2022Automation of Property Acquisition of Single Track Depositions Manufactured through Direct Energy Depositioncitations
- 2022Thermal study of a cladding layer of Inconel 625 in Directed Energy Deposition (DED) process using a phase-field modelcitations
- 2020Smart Data Visualisation as a Stepping Stone for Industry 4.0-a Case Study in Investment Casting Industrycitations
- 2020Automatic Visual Inspection of Turbo Vanes produced by Investment Casting Processcitations
- 2019Fracture characterization of a cast aluminum alloy aiming machining simulationcitations
- 2019Mechanical characterization of the AlSi9Cu3 cast alloy under distinct stress states and thermal conditionscitations
- 2017Simulation Studies of Turning of Aluminium Cast Alloy Using PCD Toolscitations
- 2017Comparison Between Cemented Carbide and PCD Tools on Machinability of a High Silicon Aluminum Alloycitations
- 2016Laboratory performance of universal adhesive systems for luting CAD/CAM restorative materials
- 2016Development of a Flexible, Light Weight Structure, Adaptable to any Space through a Shape Shifting Featurecitations
- 2016Integrated thermomechanical model for forming of glass containerscitations
- 2012Damage Prediction in Incremental Forming by Using Lemaitre Damage Modelcitations
- 2012Custom Hip Prostheses by Integrating CAD and Casting Technology
- 2002Finite-element simulation and experimental validation of a plasticity model of texture and strain-induced anisotropy
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article
Automation of Property Acquisition of Single Track Depositions Manufactured through Direct Energy Deposition
Abstract
Metallic additive manufacturing processes have been significantly developed since their inception with modern systems capable of manufacturing components for structural applications. However, successful processing through these methods requires extensive experimentation before optimised parameters can be found. In laser-based processes, such as direct energy deposition, it is common for single track beads to be deposited and subjected to analysis, yielding information on how the input parameters influence characteristics such as the output's adhesion to the substrate. These characteristics are often determined using specialised software, from images obtained by cross-section cutting the line beads. The proposed approach was based on a Python algorithm, using the scikit-image library and optical microscopy imaging from produced 18Ni300 Maraging steel on H13 tool steel, and it computes the relevant properties of DED-produced line beads, such as the track height, width, penetration, wettability angles, cross-section areas above and below the substrate and dilution proportion. 18Ni300 Maraging steel depositions were optimised with a laser power of 1550 <mml:semantics>W</mml:semantics>, feeding rate of 12 <mml:semantics>g</mml:semantics> <mml:semantics>min-1</mml:semantics>, scanning speed of 12 <mml:semantics>mm s-1</mml:semantics>, shielding gas flow rate of 25 <mml:semantics>L</mml:semantics> <mml:semantics>min-1</mml:semantics> and carrier gas flow rate of 4 <mml:semantics>L</mml:semantics> <mml:semantics>min-1</mml:semantics> for a laser spot diameter of <mml:semantics>2.1mm</mml:semantics>. Out of the cross-sectioned beads, their respective height, width and penetration were calculated with <mml:semantics>2.71%</mml:semantics>, <mml:semantics>4.01%</mml:semantics> and <mml:semantics>9.35%</mml:semantics> errors; the dilution proportion was computed with <mml:semantics>14.15%</mml:semantics> error, the area above the substrate with <mml:semantics>5.27%</mml:semantics> error and the area below the substrate with <mml:semantics>17.93%</mml:semantics> error. The average computational time for the processing of one image was <mml:semantics>12.7</mml:semantics> <mml:semantics>s</mml:semantics>. The developed approach was purely segmentational and could potentially benefit from machine-learning implementations.