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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
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article
An Investigation into the Friction of Cold-Rolled Low-Carbon DC06 Steel Sheets in Sheet Metal Forming Using Radial Basis Function Neural Networks
Abstract
<jats:p>This article presents the friction test results for cold-rolled low-carbon DC06 steel sheets, which are commonly processed into finished products using sheet metal forming methods. A strip drawing test with flat dies was used in the experimental tests. The strip-drawing test is used to model the friction phenomena in the flange area of the drawpiece. The tests were carried out using a tester that enabled lubrication with a pressurised lubricant. The friction tests were carried out at different nominal pressures, oil pressures, and friction conditions (dry friction and oil lubrication). Oils destined for deep-drawing operations were used as lubricants. Neural networks with radial base functions (RBFs) were used to explore the influence of individual friction parameters on the value of the coefficient of friction (COF). Under lubrication with both oils considered, the value of the COF increased with decreasing oil pressure. This confirms the correctness of the concept of the device for reducing friction in the flange area of the drawpiece. The developed concept of pressurised lubrication is most effective at relatively small nominal pressures of 2–4 MPa. This range of nominal pressures corresponds to the actual nip pressures when forming deep-drawing steel sheets. Under conditions of dry friction, the values obtained for the COF rise above 0.3, while under lubrication conditions, even without pressure-assisted lubrication, the COF does not exceed 0.2. As the nominal pressure increases, the effectiveness of the lubrication exponentially decreases. It was found that the Sq parameter carries the most information regarding the value of the COF. The RBF neural network with nine neurons in the hidden layer (RBF-8-9-1) and containing the Sq parameter as the input was characterised by an R2 of 0.989 and an error of 0.000292 for the testing set.</jats:p>