| 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 |
|
Moreira, Pmgp
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (19/19 displayed)
- 2020Experimental and numerical study of the dynamic response of an adhesively bonded automotive structurecitations
- 2019Oxidative Treatment of Multi-Walled Carbon Nanotubes and its Effect on the Mechanical and Electrical Properties of Green Epoxy based Nano-Compositescitations
- 2018Parameter optimisation of friction stir welded dissimilar polymers jointscitations
- 2016Mixed-mode fatigue crack propagation rates of current structural steels applied for bridges and towers construction
- 2016Modified CCS fatigue crack growth model for the AA2019-T851 based on plasticity-induced crack-closurecitations
- 2016Fatigue crack growth behaviour of the 6082-T6 aluminium using CT specimens with distinct notchescitations
- 2016Crack Closure Effects on Fatigue Crack Propagation Rates: Application of a Proposed Theoretical Modelcitations
- 2015Fatigue life prediction based on crack growth analysis using an equivalent initial flaw size model: Application to a notched geometrycitations
- 2015Ultimate tensile strength optimization of different FSW aluminium alloy jointscitations
- 2014Friction stir welded T-joints optimizationcitations
- 2014Friction stir welded butt joints optimizationcitations
- 2013A Contribution to the Mechanical Characterization of Cu ETP Used in the Electric Motors Industry
- 2012Fatigue and fracture behaviour of friction stir welded aluminium-lithium 2195citations
- 2010Fibre Bragg grating sensors for monitoring the metal inert gas and friction stir welding processescitations
- 2008A study on the effects of dented surfaces on rolling contact fatiguecitations
- 2008Fatigue crack growth in friction stir welds of 6082-T6 and 6061-T6 aluminium alloys: A comparisoncitations
- 2007Assessment of the fatigue behaviour of friction stir welded joints: Aluminium alloy 6082-T6
- 2007Fatigue behaviour of FSW and MIG weldments for two aluminium alloyscitations
- 2007Temperature field acquisition during gas metal arc welding using thermocouples, thermography and fibre Bragg grating sensorscitations
Places of action
| Organizations | Location | People |
|---|
article
Friction stir welded T-joints optimization
Abstract
The increasing use of aluminium alloys in transportation industry, such as railways, shipbuilding and aeronautics, promotes the development of more efficient and reliable welding processes. Friction stir welding (FSW) is a prominent solid-state joining technology that arose as a possible reliable welding solution. Optimized process parameters are not regularly used in previous studies found in the literature, in particular T-joints, which difficult the process industrial application. This study is focused on the optimization of friction stir welded T-joints using the Taguchi method. Mechanical tests of 27 different welded joints were carried out, and results were analysed using ANOVA, mean effect and response surface methodology (RSM). The tool rotational speed was verified to be the most influent factor in the joint mechanical properties, and is strongly dependent on the shoulder/probe diameters ratio. It was also shown that using 1000 rpm, 3.90 mm of probe depth and shoulder/probe diameters ratio of 2.5 (shoulder diameter of 15 mm) it may be achieved improved joint strength. For the optimized parameters it was verified that the welding speed does not have a significant influence. Equations to predict the joints mechanical properties were also derived through multiple regression.