| 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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Chan, Chi-Wai
University of Chester
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (11/11 displayed)
- 2024Numerical modelling and experimental validation of selective laser melting processes using a custom argon chamber setup for 316L stainless steel and Ti6AI4Vcitations
- 2024Deep learning and image data-based surface cracks recognition of laser nitrided Titanium alloycitations
- 2021A promising laser nitriding method for the design of next generation orthopaedic implants: Cytotoxicity and antibacterial performance of titanium nitride (TiN) wear nano-particles, and enhanced wear properties of laser-nitrided Ti6Al4V surfacescitations
- 2020Creating an antibacterial surface on beta TNZT alloys for hip implant applications by laser nitridingcitations
- 2019Fibre laser treatment of beta TNZT titanium alloys for load-bearing implant applications: Effects of surface physical and chemical features on mesenchymal stem cell response and Staphylococcus aureus bacterial attachmentcitations
- 2018Fibre laser treatment of martensitic NiTi alloys for load-bearing implant applications: Effects of surface chemistry on inhibiting Staphylococcus aureus biofilm formationcitations
- 2017Enhancing the antibacterial performance of orthopaedic implant materials by fibre laser surface engineeringcitations
- 2015Laser surface treatment of polyamide and NiTi alloy and the effects on mesenchymal stem cell response
- 2015Twinning anisotropy of tantalum during nanoindentationcitations
- 2014Twinning anisotropy of tantalum during nanoindentation.citations
- 2014Effect of laser treatment on the attachment and viability of mesenchymal stem cell responses on shape memory NiTi alloycitations
Places of action
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article
Deep learning and image data-based surface cracks recognition of laser nitrided Titanium alloy
Abstract
Laser nitriding, a high-precision surface modification process, enhances the hardness, wear resistance and corrosion resistance of the materials. However, laser nitriding process is prone to appearance of cracks when the process is performed at high laser energy levels. Traditional techniques to detect the cracks are time consuming, costly and lack standardization. Thus, this research aims to put forth deep learning-based crack recognition for the laser nitriding of Ti–6Al–4V alloy. The process of laser nitriding has been performed by varying duty cycles, and other process parameters. The laser nitrided sample has then been processed through optical 3D surface measurements (Alicona Infinite Focus G5), creating high resolution images. The images were then pre-processed which included 2D conversion, patchification, image augmentation and subsequent removal of anomalies. After preprocessing, the investigation focused on employing robust binary classification method based on CNN models and its variants, including ResNet-50, VGG-19, VGG-16, GoogLeNet (Inception V3), and DenseNet-121, to recognize surface cracks. The performance of these models has been optimized by fine tuning different hyper parameters and it is found that CNN base model along with models having less trainable parameters like VGG-19, VGG-16 exhibit better performance with accuracy of more than 98% to recognize cracks. Through the achieved results, it is found that VGG-19 is the most preferable model for this crack recognition problem to effectively recognize the surface cracks on laser nitrided Ti–6Al–4V material, owing to its best accuracy and lesser parameters compared to complex models like ResNet-50 and Inception-V3.