| People | Locations | Statistics |
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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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Chizari, Mahmoud
University of Hertfordshire
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (19/19 displayed)
- 2023Influence of Conventional Shot Peening Treatment on the Service Life Improvement of Bridge Steel Piles Subjected to Sea Wave Impactcitations
- 2022Analytical Modelling of Electromagnetic Bulging of Thin Metallic Tubes
- 2022Detection and Analysis of Corrosion and Contact Resistance Faults of TiN and CrN Coatings on 410 Stainless Steel as Bipolar Plates in PEM Fuel Cells
- 2021Numerical and experimental investigation of impact on bilayer aluminumrubber composite plate
- 2021Analytical Modelling of Electromagnetic Bulging of Thin Metallic Tubes
- 2021Numerical and experimental investigation of impact on bilayer aluminum-rubber composite plate
- 2021Experimental investigation of quasi-static behavior of composite and fiber metal laminate panels modified by graphene nanoplateletscitations
- 2021Experimental investigation of quasi-static behavior of composite and fiber metal laminate panels modified by graphene nanoplatelets
- 2020Numerical and experimental investigation of impact on bilayer aluminumrubber composite platecitations
- 2020Glass Fiber/Polypropylene composites with Potential of Bone Fracture Fixation Plates: Manufacturing Process and Mechanical Characterizationcitations
- 2020Verification of stress model in dissimilar materials of varying cladded pipes using a similar cladded plate model
- 2020Applications of ultrasonic testing and machine learning methods to predict the static & fatigue behavior of spot-welded jointscitations
- 2019Assessment of weld overlays in cladded piping systems with varied thicknesses
- 2019ASSESSMENT OF WELD OVERLAYS IN CLADDED PIPING SYSTEMS WITH VARIED THICKNESSES
- 2017Thermal Analysis of Cladded Pipe at a Joint Connection
- 2017Thermal analysis of girth welded joints of dissimilar metals in pipes with varying clad thicknessescitations
- 2016Behaviour of columns made from high strength steel
- 2009Effect of flyer shape on the bonding criteria in impact welding of platescitations
- 2008Experimental and numerical study of water jet spot weldingcitations
Places of action
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article
Applications of ultrasonic testing and machine learning methods to predict the static & fatigue behavior of spot-welded joints
Abstract
Ultrasonic Testing (UT) is one of the well-known Non-Destructive Techniques (NDT) of spot-weld inspection in the advanced industries, especially in automotive industry. However, the relationship between the UT results and strength of the spot-welded joints subjected to various loading conditions isunknown. The main purpose of this research is to present an integrated search system as a new approach for assessment of tensile strength and fatigue behavior of the spot-welded joints. To this end, Resistance Spot Weld (RSW) specimens of three-sheets were made of different types of low carbon steel. Afterward, the ultrasonic tests were carried out and the pulse-echo data of each sample were extracted utilizing Image Processing Technique (IPT). Several experiments (tensile and axial fatigue tests) were performed to study the mechanical properties of RSW joints of multiple sheets. The novel approach of the present research is to provide a new methodology for static strength and fatigue life assessment of three-sheets RSW joints based on the UT results by utilizing Artificial Neural Network (ANN) simulation. Next, Genetic Algorithm (GA) was used to optimize the structure of ANN. This approach helps to decrease the number of tests and the cost of performing destructive tests with appropriate reliability.