| People | Locations | Statistics |
|---|---|---|
| Naji, M. |
| |
| Motta, Antonella |
| |
| Aletan, Dirar |
| |
| Mohamed, Tarek |
| |
| Ertürk, Emre |
| |
| Taccardi, Nicola |
| |
| Kononenko, Denys |
| |
| Petrov, R. H. | Madrid |
|
| Alshaaer, Mazen | Brussels |
|
| Bih, L. |
| |
| Casati, R. |
| |
| Muller, Hermance |
| |
| Kočí, Jan | Prague |
|
| Šuljagić, Marija |
| |
| Kalteremidou, Kalliopi-Artemi | Brussels |
|
| Azam, Siraj |
| |
| Ospanova, Alyiya |
| |
| Blanpain, Bart |
| |
| Ali, M. A. |
| |
| Popa, V. |
| |
| Rančić, M. |
| |
| Ollier, Nadège |
| |
| Azevedo, Nuno Monteiro |
| |
| Landes, Michael |
| |
| Rignanese, Gian-Marco |
|
Kumar, Raman
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (19/19 displayed)
- 2024Study on Nanomaterials Coated Natural Coir Fibers as Crack Arrestor in Cement Compositecitations
- 2024Study on Nanomaterials Coated Natural Coir Fibers as Crack Arrestor in Cement Compositecitations
- 2023Optimization study on wear behaviour of aluminium 7075 hybrid composite containing silicon carbide and aluminium oxide using Taguchi methodcitations
- 2023Asymmetric/Symmetric Glass-Fibre-Filled Polyamide 66 Gears—A Systematic Fatigue Life Studycitations
- 2023Investigation of copper reinforced Acrylonitrile Butadiene Styrene and Nylon 6 based thermoplastic polymer nanocomposite filaments for 3D printing of electronic componentscitations
- 2023Additive manufacturing and characterization of titanium wall used in nuclear applicationcitations
- 2023Characterisation of additively manufactured titanium wall: Mechanical and microstructural aspectscitations
- 2022PbS nanoparticles anchored 1D- CdSe nanowires:Core-shell design towards energy storage supercapacitor applicationcitations
- 2022Implementation of Taguchi and Genetic Algorithm Techniques for Prediction of Optimal Part Dimensions for Polymeric Biocomposites in Fused Deposition Modelingcitations
- 2022Implementation of Taguchi and Genetic Algorithm Techniques for Prediction of Optimal Part Dimensions for Polymeric Biocomposites in Fused Deposition Modelingcitations
- 2022Investigation of Tool Wear Rate during EDM for Aluminium Metal Matrix Composite (5-10% TiB2) Prepared by Squeeze Castingcitations
- 2022Performance Comparison and Critical Finite Element Based Experimental Analysis of Various Forms of Reinforcement Retaining Structural Systemcitations
- 2021Investigations on melt flow rate and tensile behaviour of single, double and triple sized copper reinforced thermo-plastic compositescitations
- 2021Environmental, economical and technological Analysis of MQL assisted machining of Al-Mg-Zr Alloy using PCD toolcitations
- 2020Impact of process parameters of resistance spot welding on mechanical properties and micro hardness of stainless steel 304 weldmentscitations
- 2020Metal spray layered hybrid additive manufacturing of PLA composite structures: Mechanical, thermal and morphological propertiescitations
- 2020Investigating the influence of WEDM process parameters in machining of hybrid aluminum compositescitations
- 2020Non-Conventional Technique of Machining and Metallization of Polymer Components
- 2020Mechanical Strength Enhancement of 3D Printed Acrylonitrile Butadiene Styrene Polymer Components Using Neural Network Optimization Algorithmcitations
Places of action
| Organizations | Location | People |
|---|
article
Mechanical Strength Enhancement of 3D Printed Acrylonitrile Butadiene Styrene Polymer Components Using Neural Network Optimization Algorithm
Abstract
<jats:p>Fused filament fabrication (FFF), a portable, clean, low cost and flexible 3D printing technique, finds enormous applications in different sectors. The process has the ability to create ready to use tailor-made products within a few hours, and acrylonitrile butadiene styrene (ABS) is extensively employed in FFF due to high impact resistance and toughness. However, this technology has certain inherent process limitations, such as poor mechanical strength and surface finish, which can be improved by optimizing the process parameters. As the results of optimization studies primarily depend upon the efficiency of the mathematical tools, in this work, an attempt is made to investigate a novel optimization tool. This paper illustrates an optimization study of process parameters of FFF using neural network algorithm (NNA) based optimization to determine the tensile strength, flexural strength and impact strength of ABS parts. The study also compares the efficacy of NNA over conventional optimization tools. The advanced optimization successfully optimizes the process parameters of FFF and predicts maximum mechanical properties at the suggested parameter settings.</jats:p>