A5008880379- Additive Manufacturing Materials and Processes
- Additive Manufacturing and 3D Printing Technologies
- High Entropy Alloys Studies
- Welding Techniques and Residual Stresses
- Metal and Thin Film Mechanics
- Metallurgy and Material Forming
- Metal Alloys Wear and Properties
- Manufacturing Process and Optimization
- Titanium Alloys Microstructure and Properties
- Metal Forming Simulation Techniques
- Pharmaceutical and Antibiotic Environmental Impacts
- Advanced Machining and Optimization Techniques
- Aluminum Alloys Composites Properties
- Microbial Community Ecology and Physiology
- Microstructure and mechanical properties
- High-Temperature Coating Behaviors
- Laser and Thermal Forming Techniques
- Machine Learning in Materials Science
- Fisheries and Aquaculture Studies
- High Temperature Alloys and Creep
- Innovation and Socioeconomic Development
- Electrospun Nanofibers in Biomedical Applications
- Microstructure and Mechanical Properties of Steels
- Probabilistic and Robust Engineering Design
- 3D Printing in Biomedical Research
University of Liège
2015-2023
Hanoi University of Science and Technology
2023
CRM Group (Belgium)
2022-2023
Thu Dau Mot University
2022
Dalat University
2022
UCLouvain
2021
This work focuses on the thermal modeling of Directed Energy Deposition a composite coating (316L stainless steel reinforced by Tungsten carbides) 316L substrate. The developed finite element model predicts history and melt pool dimension evolution in middle section clad during deposition. Numerical results were correlated with experimental analysis (light optical scanning electron microscopies thermocouple records) to validate discuss possible solidification mechanisms. It was proven that...
This paper reports the sensitivity of thermal and displacement histories predicted by a finite element analysis to material properties boundary conditions directed-energy deposition M4 high speed steel thin-wall part additively manufactured on 42CrMo4 substrate. The model accuracy was assessed comparing simulation results with experimental measurements such as evolving local temperatures distortion numerical history were successfully correlated solidified microstructures measured scanning...
In the last decade, machine learning is increasingly attracting researchers in several scientific areas and, particular, additive manufacturing field. Meanwhile, this technique remains as a black box for many researchers. Indeed, it allows obtaining novel insights to overcome limitation of classical methods, such finite element method, and take into account multi-physical complex phenomena occurring during process. This work presents comprehensive study implementing (artificial neural...
In this study, a data-driven deep learning model for fast and accurate prediction of temperature evolution melting pool size metallic additive manufacturing processes are developed. The study focuses on bulk experiments the M4 high-speed steel material powder manufactured by Direct Energy Deposition. Under non-optimized process parameters, many deposited layers (above 30) generate large changes microstructure through sample depth caused high sensitivity cladding thermal history. A 2D finite...
The advancement of additive manufacturing technology or 3-Dimesion printing (3D printing) allows for the creation parts with intricate designs, resulting in less material waste compared to conventional methods. Although current 3D printers primarily use plastic metal materials, there is a growing interest using biomaterials printing. To facilitate this trend, developing and designing capable hydrogel materials crucial. In research, printer direct indirect extruders designed, calculated,...
This study quantifies the effects of uncertainty raised from process parameters, material properties, and boundary conditions in directed energy deposition (DED) M4 High-Speed Steel using deep learning (DL)-based probabilistic approach. A DL-based surrogate model is first constructed data obtained a finite element (FE) model, which was validated against experiment. Then, sources are characterized by method propagated Monte-Carlo (MC) method. Lastly, sensitivity analysis (SA) variance-based...
A finite element simulation of the steel shell formation in continuous casting has been developed. The current research is focused on solidification molten during initial stages mould cooling. model allows predicting temperature field throughout process: gradient, front, cooling rates. In stationary state, prediction thickness reasonably agrees with analytical models and experimental observations. tool used to study alteration thermal case sticking defects encountered industrial practice....
Laser powder bed fusion (LPBF) is an additive manufacturing technique that widely used to produce AlSi10Mg parts with a good strength-to-weight ratio and very fine microstructure thanks high cooling rates. However, obtain better mechanical properties, ductility higher fatigue resistance, post-treatments have be performed. In this work, friction stir processing, thermomechanical post-treatment, applied on as-built plate of 5 mm thickness. This post-treatment leads decrease the percentage...