A finite-element simulation of asperity flattening in metal forming

Ma, Benben; Lü, Cheng; Jiang, Zhengyi

doi:10.1016/s0924-0136(02)00752-5

Cited by 15 publications

(10 citation statements)

References 7 publications

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“…Among the widespread usage of regression analysis in tribology, it is necessary to put special emphasis on the possibility of the determination of the friction co-efficiency [20] and the determination of the wear rate for different combinations of load, grit size, and sliding distance [5,13,24]. To develop a contact area ratio expression regarding the nominal pressure, the friction coefficient and relative sliding of the MATLAB programme was utilized [15]. Regression equations have led to modelling and predicting the film thickness in contact conditions under the elastohydrodynamic lubrication [11,16].…”

Section: Introductionmentioning

confidence: 99%

FRICTION MODELING OF Al-Mg ALLOY SHEETS BASED ON MULTIPLE REGRESSION ANALYSIS AND NEURAL NETWORKS

Lemu¹,

Trzepieciński²,

Fejkiel³

2017

Adv. Sci. Technol. Res. J.

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This article reports a proposed approach to a frictional resistance description in sheet metal forming processes that enables the determination of the friction coefficient value under a wide range of friction conditions, without performing timeconsuming experiments. The motivation for this proposal is the fact that there exists a considerable amount of factors that affect the friction coefficient value and as a result building analytical friction model for specified process conditions is practically impossible. In this proposed approach, a mathematical model of friction behaviour is created using multiple regression analysis and artificial neural networks. The regression analysis was performed using a subroutine in MATLAB programming code and STATISTICA Neural Networks was utilized to build an artificial neural networks model. The effect of different training strategies on the quality of neural networks was studied. As input variables for regression model and training of radial basis function networks, generalized regression neural networks and multilayer networks, the results of strip drawing friction test were utilized. Four kinds of Al-Mg alloy sheets were used as a test material.

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Section: Introductionmentioning

confidence: 99%

FRICTION MODELING OF Al-Mg ALLOY SHEETS BASED ON MULTIPLE REGRESSION ANALYSIS AND NEURAL NETWORKS

Lemu¹,

Trzepieciński²,

Fejkiel³

2017

Adv. Sci. Technol. Res. J.

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“…Shear investigations using inelastic material behavior can be found e.g. in [21][22][23][24][25], however, without dealing with the description of the stick-slip contact in detail.…”

Section: Introductionmentioning

confidence: 99%

A finite element model to simulate the physical mechanisms of wear and crack initiation in wheel/rail contact

Daves

Kubin

Scheriau

et al. 2016

Wear

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“…Surface changes due to asperity deformations influence the load-carrying capacity of the lubricant [12]. Ma et al [13] calculated the asperity flattening in metal forming and showed that the area of contact ratio increased as a function of normal pressure. Furthermore the relative sliding between a tool and a sheet can increase the area of the contact ratio.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of friction coefficient of an auto-body steel sheet

2016

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In this paper the results of strip drawing tests aimed to determine the friction coefficient in sheet metal forming operations are presented. The tests were conducted using a specially designed tribological simulator. The deep drawing quality steel sheet used in the automotive industry was tested. The relationship that shows the effect of sheet normal load, tool surface roughness, lubrication conditions and sample orientation according the rolling direction of the sheet on the value of friction coefficient are presented and discussed. The Scanning Electron Microscope (SEM) micrographs of sheet surfaces after the friction test allowed us to identify the mechanisms that occur at the contact of two bodies with rough surfaces. The results of the tests indicate that the relationship between friction force and normal force is nonlinear. Thus, the value of the friction coefficient is changed with the change of the load value.

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A finite-element simulation of asperity flattening in metal forming

Cited by 15 publications

References 7 publications

FRICTION MODELING OF Al-Mg ALLOY SHEETS BASED ON MULTIPLE REGRESSION ANALYSIS AND NEURAL NETWORKS

FRICTION MODELING OF Al-Mg ALLOY SHEETS BASED ON MULTIPLE REGRESSION ANALYSIS AND NEURAL NETWORKS

A finite element model to simulate the physical mechanisms of wear and crack initiation in wheel/rail contact

Evaluation of friction coefficient of an auto-body steel sheet

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