Elastic and plastic deformation of surface asperities and their load-carrying mechanisms during the formation of a real contact area

Kalin, Mitjan; Zugelj, B. Brodnik; Lamut, M.; Hamouda, K.

doi:10.1016/j.triboint.2022.108067

Cited by 4 publications

(2 citation statements)

References 28 publications

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“…Sutcliffe [49] found that a high pressure between contacting asperities affects asperity deformation. Local contact pressures prevailing at the surface of asperities cause their elastic-plastic deformation [50]. The rate of asperity flattening with bulk straining is related to the spacing and contact pressure [51].…”

Section: Resultsmentioning

confidence: 99%

Analysis of Coefficient of Friction of Deep-Drawing-Quality Steel Sheets Using Multi-Layer Neural Networks

Trzepieciński,

Szwajka,

Szewczyk

2024

Lubricants

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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.

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Section: Resultsmentioning

confidence: 99%

Analysis of Coefficient of Friction of Deep-Drawing-Quality Steel Sheets Using Multi-Layer Neural Networks

Trzepieciński,

Szwajka,

Szewczyk

2024

Lubricants

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“…17) Under the pressure, the radial deformation of coil can be divided into two parts: own elastic deformation and additional contact deformation during interlayer rough contour compression. [18][19][20][21][22] The anisotropy of coils is actually generated by interlayer additional contact deformation. However, additional contact deformation between layers cannot be considered by traditional analytical methods.…”

Section: Introductionmentioning

confidence: 99%

Efficient Finite Element Simulation of Cold Rolled Strip Coiling Process Considering Additional Contact Deformation between Layers

Dai,

Liang,

Qiu

et al. 2024

ISIJ Int.

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When analyzing the strip coiling process, the finite element (FE) method is closer to the actual working conditions compared to the analytical method. However, due to the large number of strip elements and contact elements, it often leads to problems such as long-time consumption and nonconvergence. Meanwhile, traditional FE methods are still unable to solve the problem of additional contact deformation between layers. Therefore, in order to overcome the shortcomings of the above methods, the FE software MSC Marc is used to establish a strip coiling model. The distribution pattern of interlayer friction and contact stress are analyzed to propose a new step-by-step bonding FE model, which greatly reduces the computing time. Through laminated compression experiments, the variation curve between additional contact deformation and pressure is obtained.The curve is introduced into the gasket elements to consider the additional contact deformation between the layers, and the effect of additional contact deformation between the layers on the stress of the coil and the pressure on the mandrel is studied. Finally, the analytical solution is compared with the FE solution proposed in this paper, and the errors generated by the analytical method are analyzed.

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Fractal theory-based contact analysis of surface microtextured involute spline coupling

Xue,

Lin,

et al. 2024

Tribology International

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Elastic and plastic deformation of surface asperities and their load-carrying mechanisms during the formation of a real contact area

Cited by 4 publications

References 28 publications

Analysis of Coefficient of Friction of Deep-Drawing-Quality Steel Sheets Using Multi-Layer Neural Networks

Analysis of Coefficient of Friction of Deep-Drawing-Quality Steel Sheets Using Multi-Layer Neural Networks

Efficient Finite Element Simulation of Cold Rolled Strip Coiling Process Considering Additional Contact Deformation between Layers

Fractal theory-based contact analysis of surface microtextured involute spline coupling

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