| 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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Szewczyk, Marek
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (14/14 displayed)
- 2025Experimental Study on Mechanical Performance of Single-Side Bonded Carbon Fibre-Reinforced Plywood for Wood-Based Structures
- 2024Analysis of the Microstructure and Mechanical Performance of Resistance Spot-Welding of Ti6Al4V to DP600 Steel Using Copper/Gold Cold-Sprayed Interlayerscitations
- 2024Effect of Countersample Coatings on the Friction Behaviour of DC01 Steel Sheets in Bending-under-Tension Friction Testscitations
- 2024Application of categorical boosting to modelling the friction behaviour of DC05 steel sheets in strip drawing testcitations
- 2024Analysis of the friction performance of deep-drawing steel sheets using network modelscitations
- 2024The Effect of the Addition of Silicon Dioxide Particles on the Tribological Performance of Vegetable Oils in HCT600X+Z/145Cr46 Steel Contacts in the Deep-Drawing Process
- 2024Analysis of Influence of Coating Type on Friction Behaviour and Surface Topography of DC04/1.0338 Steel Sheet in Bending Under Tension Friction Test
- 2024Analysis of Coefficient of Friction of Deep-Drawing-Quality Steel Sheets Using Multi-Layer Neural Networkscitations
- 2023Pressure-Assisted Lubrication of DC01 Steel Sheets to Reduce Friction in Sheet-Metal-Forming Processescitations
- 2023Assessment of the Tribological Performance of Bio-Based Lubricants Using Analysis of Variancecitations
- 2023An Investigation into the Friction of Cold-Rolled Low-Carbon DC06 Steel Sheets in Sheet Metal Forming Using Radial Basis Function Neural Networkscitations
- 2022The Use of Non-Edible Green Oils to Lubricate DC04 Steel Sheets in Sheet Metal Forming Processcitations
- 2022Analysis of the Friction Mechanisms of DC04 Steel Sheets in the Flat Strip Drawing Testcitations
- 2022Frictional Characteristics of Deep-Drawing Quality Steel Sheets in the Flat Die Strip Drawing Testcitations
Places of action
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article
Application of categorical boosting to modelling the friction behaviour of DC05 steel sheets in strip drawing test
Abstract
<jats:p>It is challenging to model the coefficient of friction, surface roughness, and related tribological processes during metal contact because of flattening, ploughing, and adhesion. It is important to choose the appropriate process parameters carefully when creating analytical models to overcome the challenges posed by complexity. This will ensure the production of sheet metal formed components that meets the required quality standards and is free from faults. This research analyses the impacts of nominal pressure, kinematic viscosity of lubricant, and lubricant pressure on the coefficient of friction and average roughness of DC05 deep-drawing steel sheets. The strip drawing test was used to determine the coefficient of friction. This work utilises the Categoric Boosting (CatBoost) machine learning algorithm created by Yandex to estimate the COF and surface roughness, intending to conduct a comprehensive investigation of process parameters. A Shapley decision plot exhibits the coefficient of friction prediction models via cumulative SHapley Additive exPlanations (SHAP) data. CatBoost has outstanding prediction accuracy, as seen by R2 values ranging from 0.955 to 0.894 for both the training and testing datasets for the COF, as well as 0.992 to 0.885 for surface roughness.</jats:p>