Assessment of the effectiveness of lubrication of Ti-6Al-4V titanium alloy sheets using radial basis function neural networks

Trzepieciński, Tomasz; Szpunar, Marcin

doi:10.14311/ap.2021.61.0489

Cited by 4 publications

(2 citation statements)

References 30 publications

(36 reference statements)

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“…The oils used are generally available and several times cheaper than professional oils intended for deep-drawing operations. Previous investigations on friction of titanium alloy sheets [ 38 ] confirmed the effectiveness of typical engine and gear oils in reducing the COF.…”

Section: Methodsmentioning

confidence: 78%

Frictional Characteristics of Deep-Drawing Quality Steel Sheets in the Flat Die Strip Drawing Test

2022

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Friction is one of the most important technological phenomena and has a large influence on the flow characteristics of a deformed material. A strip drawing friction test was used to evaluate the friction characteristics of 0.8 mm thick DC04 steel sheets in a sheet forming operation. Friction tests were carried out using a specially designed friction simulator and uniaxial tensile tests were carried out to determine the mechanical properties of the specimens. In addition, measurements of the sheet surface topography were carried out to identify the tribological properties of the specimens. The friction tests were conducted under different pressure and lubrication conditions. A comparative analysis of the results of the friction tests revealed different changes in the surface topography of the test sheets which can be associated with specific friction mechanisms. It was found that the effectiveness of lubrication depends on the lubricant viscosity and nominal pressure. Increasing the nominal pressure intensifies the phenomenon of asperity flattening and reduces the volume of closed pockets of lubricant.

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Section: Methodsmentioning

confidence: 78%

Frictional Characteristics of Deep-Drawing Quality Steel Sheets in the Flat Die Strip Drawing Test

2022

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“…They found that the ML models are able to predict the wear behaviour of several grades of 2xxx-7xxx series aluminium alloys. Trzepieci ński and Szpunar [24] used ANNs to investigate the effectiveness of vegetable-oil-based lubricants in reducing the friction of Ti-6Al-4V material. The response surfaces of the neural networks allowed for finding the relationship between the contact pressure and oil viscosity and the coefficient of friction.…”

Section: Introductionmentioning

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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The Use of Non-Edible Green Oils to Lubricate DC04 Steel Sheets in Sheet Metal Forming Process

2022

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Lubrication is a basic and relatively effective way to reduce friction in sheet metal forming operations. The drive to eliminate synthetic and mineral oils, which are difficult to recycle, from the manufacturing process has opened up opportunities for the use of vegetable-based bio-lubricants. This article presents a comparison of the lubrication performance of two non-edible oils (karanja and moringa) with the most frequently tested edible oils (sunflower and rape-seed). Deep drawing quality low-carbon steel sheets DC04, commonly used in the automotive industry, were used as the test material. Friction tests were carried out under various lubricants and normal pressures in the range between 3 and 12 MPa using the strip drawing test. Furthermore, a study was also made of the effect of a change in the surface topography and the mechanical properties of the sheet metal due to plastic deformation resulting from friction. It was found that under the most favorable lubrication conditions (sample pre-strain 21%, nominal pressure 6 MPa), karanja oil reduced the coefficient of friction by approximately 33%. Both non-edible lubricants provided the best lubrication when testing samples pre-strained at 7% under the whole range of nominal pressures. It was also revealed that in the case of the smallest pre-straining of the specimens (7%), karanja oil was the most effective within nominal pressures of 3–6 MPa, while at higher pressures (9–12 MPa), the moringa oil lowered the value of the coefficient of friction to a greater extent.

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Assessment of the effectiveness of lubrication of Ti-6Al-4V titanium alloy sheets using radial basis function neural networks

Cited by 4 publications

References 30 publications

Frictional Characteristics of Deep-Drawing Quality Steel Sheets in the Flat Die Strip Drawing Test

Frictional Characteristics of Deep-Drawing Quality Steel Sheets in the Flat Die Strip Drawing Test

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

The Use of Non-Edible Green Oils to Lubricate DC04 Steel Sheets in Sheet Metal Forming Process

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