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 |
|
Gajević, Sandra
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (17/17 displayed)
- 2024Magnesium-Titanium Alloys: A Promising Solution for Biodegradable Biomedical Implantscitations
- 2024Investigation of the impact of abrasive action on surface roughness and worn mass of laminated composites
- 2024Tribological Behaviour of Hypereutectic Al-Si Composites: A Multi-Response Optimisation Approach with ANN and Taguchi Grey Methodcitations
- 2024Multi-Objective Optimization of Tribological Characteristics for Aluminum Composite Using Taguchi Grey and TOPSIS Approachescitations
- 2024Optimization of Dry Sliding Wear in Hot-Pressed Al/B4C Metal Matrix Composites Using Taguchi Method and ANNcitations
- 2024Progress in Aluminum-Based Composites Prepared by Stir Casting: Mechanical and Tribological Properties for Automotive, Aerospace, and Military Applicationscitations
- 2023Optimization of tribological behaviour of hybrid composites based on A356 and ZA-27 alloys
- 2023Wear of A356/Al2O3 nanocomposites and optimisation of material and operating parameters
- 2023Influence of materials on the efficiency of worm gear transmission
- 2023A review on mechanical and tribological properties of aluminium-based metal matrix nanocomposites
- 2023Comparative analysis of hybrid composites based on A356 and ZA-27 alloys regarding their tribological behaviourcitations
- 2023Hypereutectic aluminum alloys and composites: A reviewcitations
- 2023Tribological Application of Nanocomposite Additives in Industrial Oilscitations
- 2022Optimization of parameters that affect wear of A356/Al<sub>2</sub>O<sub>3</sub> nanocomposites using RSM, ANN, GA and PSO methodscitations
- 2021Multi response parameters optimization of ZA-27 nanocompositescitations
- 2021Optimization of hybrid ZA‐27 nanocomposites using ANOVA and ANN analysis
- 2014Application of Taguchi methods in testing tensile strength of polyethylene
Places of action
Organizations | Location | People |
---|
article
Multi-Objective Optimization of Tribological Characteristics for Aluminum Composite Using Taguchi Grey and TOPSIS Approaches
Abstract
<jats:p>In this study, a multi-objective optimization regarding the tribological characteristics of the hybrid composite with a base material of aluminum alloy A356 as a constituent, reinforced with a 10 wt.% of silicon carbide (SiC), size 39 µm, and 1, 3, and 5 wt.% graphite (Gr), size 35 µm, was performed using the Taguchi method, gray relational analysis (GRA), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) decision-making methods. Tribological tests were carried out on a “block on disc” type tribometer with lubrication. Load, sliding speed, and graphite mass concentration were analyzed as input parameters. As output parameters, wear rate and coefficient of friction were calculated. An analysis of variance (ANOVA) was conducted to identify all parameters that have a significant influence on the output multi-response. It was found that the normal load has the highest influence of 41.86%, followed by sliding speed at 32.48% and graphite addition at 18.47%, on the tribological characteristics of composites. Multi-objective optimization determined that the minimal wear rate and coefficient of friction are obtained when the load is 40 N, the sliding speed is 1 m/s, and the composite contains 3 wt.% Gr. The optimal combination of parameters achieved by GRA was also confirmed by the TOPSIS method, which indicates that both methods can be used with high reliability to optimize the tribological characteristics. The analysis of worn surfaces using scanning electron microscopy revealed adhesive and delamination wear as dominant mechanisms.</jats:p>