A Computational Study of the Effects of Strain Hardening in Micro-asperity Friction Models

Bhagwat, Pushkaraj; Sista, Bhargava; Vemaganti, Kumar

doi:10.1007/s11249-017-0939-0

Cited by 10 publications

(3 citation statements)

References 32 publications

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“…In general, it is desirable to keep the kinetic energy at or below 1% of the internal energy. A study to establish the quasi-static nature of the simulations is performed as in [50,51].…”

Section: Finite Element Modelmentioning

confidence: 99%

Fractal surface-based three-dimensional modeling to study the role of morphology and physiology in human skin friction

Roy,

Vemaganti

2024

Surf. Topogr.: Metrol. Prop.

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Human skin plays an important role in our perception of contact made throughout the day. In this work, we study the interplay of various morphological and physiological factors that dictate its contact mechanics. A hybrid computational-empirical approach is developed to model skin friction and to understand the role of roughness in contact mechanics of human skin variations in structural properties. A fractal rough surface is considered to model the skin surface. A layered three-dimensional finite element model is generated with stratum corneum, viable epidermis, and dermis which is further used to determine its mechanical response under normal loading. An empirical relationship is then used to predict the coefficient of friction. The effects of varying the Young’s modulus, roughness parameters, thickness of stratum corneum and domain size are studied. Simulations are performed for multiple realizations to quantify statistical variations. Our results show that the proposed approach can replicate several experimental findings from the literature such as the decrease in skin friction with humidity and increasing roughness. The study provides qualitative and quantitative insight into the role of roughness in the contact mechanics of human skin while accounting for the effects of micro-level interfacial phenomena.

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“…In general, it is desirable to keep the kinetic energy at or below 1% of the internal energy. A study to establish the quasi-static nature of the simulations is performed as in [50,51].…”

Section: Finite Element Modelmentioning

confidence: 99%

Fractal surface-based three-dimensional modeling to study the role of morphology and physiology in human skin friction

Roy,

Vemaganti

2024

Surf. Topogr.: Metrol. Prop.

Self Cite

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“…On the one hand, material properties, such as the crystal structure [3], hardness [4,5], elastic and shear modulus [6,7], grain size [8,9], and surface energy [10,11] of the contacting materials affect the frictional behaviour. On the other hand, the operational conditions, such as the normal loads [12,13], sliding velocities [14,15], environmental conditions [16,17], temperatures [18], and lubricants [19], have a major influence on the tribological behaviour.…”

Section: Introductionmentioning

confidence: 99%

A Generalised Method for Friction Optimisation of Surface Textured Seals by Machine Learning

Brase,

Binder,

Jonkeren

et al. 2024

Lubricants

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Friction behaviour is an important characteristic of dynamic seals. Surface texturing is an effective method to control the friction level without the need to change materials or lubricants. However, it is difficult to put the manual prediction of optimal friction reducing textures as a function of operating conditions into practice. Therefore, in this paper, we use machine learning techniques for the prediction of optimal texture parameters for friction optimisation. The application of pneumatic piston seals serves as an illustrative example to demonstrate the machine learning method and results. The analyses of this work are based on experimentally determined data of surface texture parameters, defined by the dimple diameter, distance, and depth. Furthermore friction data between the seal and the pneumatic cylinder are measured in different friction regimes from boundary over mixed up to hydrodynamic lubrication. A particular innovation of this work is the definition of a generalised method that guides the entire machine learning process from raw data acquisition to model prediction, without committing to only a few learning algorithms. A large number of 26 regression learning algorithms are used to build machine learning models through supervised learning to evaluate the suitability of different models in the specific application context. In order to select the best model, mathematical metrics and tribological relationships, like Stribeck curves, are applied and compared with each other. The resulting model is utilised in the subsequent friction optimisation step, in which optimal surface texture parameter combinations with the lowest friction coefficients are predicted over a defined interval of relative velocities. Finally, the friction behaviour is evaluated in the context of the model and optimal value combinations of the surface texture parameters are identified for different lubrication conditions.

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“…[32] and [33], the material is bilinear elastic-plastic with 2% tangent modulus of isotropic linear hardening. Bhagwat et al [34] reported that a larger tangent modulus results in a higher friction coefficient. Zhao et al [35] performed a similar study but on materials with power-law hardening as it is a more realistic description of behavior for many materials.…”

Section: Introductionmentioning

confidence: 99%

Model for the static friction coefficient in a full stick elastic-plastic coated spherical contact

Zhou

Etsion

2018

Friction

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Finite element analysis is used to investigate an elastic-plastic coated spherical contact in full stick contact condition under combined normal and tangential loading. Sliding inception is associated with a loss of tangential stiffness. The effect of coating thickness on the static friction coefficient is intensively investigated for the case of hard coatings. For this case, with the increase in coating thickness, the static friction coefficient first increases to its maximum value at a certain coating thickness, thereafter decreases, and eventually levels off. The effect of the normal load and material properties on this behavior is discussed. Finally, a model for the static friction coefficient as a function of the coating thickness is provided for a wide range of material properties and normal loading.

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A Computational Study of the Effects of Strain Hardening in Micro-asperity Friction Models

Cited by 10 publications

References 32 publications

Fractal surface-based three-dimensional modeling to study the role of morphology and physiology in human skin friction

Fractal surface-based three-dimensional modeling to study the role of morphology and physiology in human skin friction

A Generalised Method for Friction Optimisation of Surface Textured Seals by Machine Learning

Model for the static friction coefficient in a full stick elastic-plastic coated spherical contact

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