| People | Locations | Statistics |
|---|---|---|
| Naji, M. |
| |
| Motta, Antonella |
| |
| Aletan, Dirar |
| |
| Mohamed, Tarek |
| |
| Ertürk, Emre |
| |
| Taccardi, Nicola |
| |
| Kononenko, Denys |
| |
| Petrov, R. H. | Madrid |
|
| Alshaaer, Mazen | Brussels |
|
| Bih, L. |
| |
| Casati, R. |
| |
| Muller, Hermance |
| |
| Kočí, Jan | Prague |
|
| Šuljagić, Marija |
| |
| Kalteremidou, Kalliopi-Artemi | Brussels |
|
| Azam, Siraj |
| |
| Ospanova, Alyiya |
| |
| Blanpain, Bart |
| |
| Ali, M. A. |
| |
| Popa, V. |
| |
| Rančić, M. |
| |
| Ollier, Nadège |
| |
| Azevedo, Nuno Monteiro |
| |
| Landes, Michael |
| |
| Rignanese, Gian-Marco |
|
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
| Organizations | Location | People |
|---|
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>