| 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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Trzepieciński, Tomasz
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (26/26 displayed)
- 2025Experimental Study on Mechanical Performance of Single-Side Bonded Carbon Fibre-Reinforced Plywood for Wood-Based Structures
- 2024Predicting the Effect of RSW Parameters on the Shear Force and Nugget Diameter of Similar and Dissimilar Joints Using Machine Learning Algorithms and Multilayer Perceptroncitations
- 2024Application of machine learning and neural network models based on experimental evaluation of dissimilar resistance spot-welded joints between grade 2 titanium alloy and AISI 304 stainless steelcitations
- 2024Artificial neural networks and experimental analysis of the resistance spot welding parameters effect on the welded joint quality of AISI 304citations
- 2024Effect of Alumina Proportion on the Microstructure and Technical and Mechanical Characteristics of Zirconia-Based Porous Ceramicscitations
- 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
- 2024Eco-Friendly Synthesis of Al2O3 Nanoparticles: Comprehensive Characterization Properties, Mechanics, and Photocatalytic Dye Adsorption Studycitations
- 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
- 2024Numerical and Experimental Research of the Plastic Forming Process of Hastelloy X Alloy Sheets Using Elastomeric and Steel Tools
- 2024Analysis of Coefficient of Friction of Deep-Drawing-Quality Steel Sheets Using Multi-Layer Neural Networkscitations
- 2024Application of Powder-Bed Fusion of Metals Using a Laser for Manufacturing of M300 Maraging Steel Tools Intended for Sheet Metal Bending
- 2023Pressure-Assisted Lubrication of DC01 Steel Sheets to Reduce Friction in Sheet-Metal-Forming Processescitations
- 2023Experimental and Numerical Investigations of the Fatigue Life of AA2024 Aluminium Alloy-Based Nanocomposite Reinforced by TiO2 Nanoparticles Under the Effect of Heat Treatmentcitations
- 2023Prediction Of The Coefficient Of Friction In The Single Point Incremental Forming Of Truncated Cones From A Grade 2 Titanium Sheetcitations
- 2023An Investigation into the Friction of Cold-Rolled Low-Carbon DC06 Steel Sheets in Sheet Metal Forming Using Radial Basis Function Neural Networkscitations
- 2022Incremental sheet forming of metal-based composites used in aviation and automotive applicationscitations
- 2022The Use of Non-Edible Green Oils to Lubricate DC04 Steel Sheets in Sheet Metal Forming Processcitations
- 2022Frictional Characteristics of Deep-Drawing Quality Steel Sheets in the Flat Die Strip Drawing Testcitations
- 2021Surface Finish Analysis in Single Point Incremental Sheet Forming of Rib-Stiffened 2024-T3 and 7075-T6 Alclad Aluminium Alloy Panelscitations
- 2020Strength Analysis of a Rib-Stiffened GLARE-Based Thin-Walled Structurecitations
- 2020Residual Stresses and Surface Roughness Analysis of Truncated Cones of Steel Sheet Made by Single Point Incremental Formingcitations
- 2016Formation of microcracks near surgical defect in femur: Assessment of ultimate loading conditions
- 2015Managing the tooling service in body production line
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article
Analysis of Coefficient of Friction of Deep-Drawing-Quality Steel Sheets Using Multi-Layer Neural Networks
Abstract
<jats:p>This article presents the results of an analysis of the influence of friction process parameters on the coefficient of friction of steel sheets 1.0347 (DC03), 1.0338 (DC04) and 1.0312 (DC05). A special tribometer was designed and manufactured in order to simulate the friction phenomenon occurring in the blankholder area in deep drawing operations. Lubricant was supplied to the contact zone under pressure. The value of the coefficient of friction was determined under various contact pressures and lubrication conditions. Multi-layer artificial neural networks (ANNs) were used to predict the value of the coefficient of friction. The input parameters considered were the kinematic viscosity of lubricants, contact pressure, lubricant pressure, selected mechanical properties and basic surface roughness parameters of sheet metals. The value of the coefficient of friction of 1.0312 steel sheets was predicted based on the results of friction tests on 1.0347 and 1.0338 steel sheets. Many ANN models were built to find a neural network that will provide the best prediction performance. It was found that to ensure a high performance of ANN prediction, it is necessary to simultaneously take into account all the considered roughness parameters (Sa, Ssk and Sku). The predictive performance of the ‘best’ network was greater than R2 = 0.98. The lubricant pressure had the greatest impact on the coefficient of friction. Increasing the value of this parameter reduces the value of the coefficient of friction. However, the greater the contact pressure, the smaller the beneficial effect of pressure-assisted lubrication. The third parameter of the friction process, the kinematic viscosity of the oil, exhibited the smallest impact on the coefficient of friction.</jats:p>