The Influence of Specific Features of Load and Hydrogen Charging on Steel Tribotechnical Properties

Balitskii, Alexander; Kolesnikov, V. O.; Іvas’kevych, L. М.; Havrylyuk, M. R.

doi:10.1007/s11003-023-00691-5

Cited by 2 publications

(1 citation statement)

References 32 publications

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“…In processes occurring at elevated temperatures, the lubricant should be resistant to high temperatures and have stable physicochemical properties over a wide temperature range [19]. During sliding, the surface fractures via the tearing of the material under an intense thermal setting [20].…”

Section: Introductionmentioning

confidence: 99%

The Comparison of the Multi-Layer Artificial Neural Network Training Methods in Terms of the Predictive Quality of the Coefficient of Friction of 1.0338 (DC04) Steel Sheet

Trzepieciński

2024

Materials

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Friction is one of the main phenomena accompanying sheet metal forming methods, affecting the surface quality of products and the formability of the sheet metal. The most basic and cheapest way to reduce friction is to use lubricants, which should ensure the highest lubrication efficiency and at the same time be environmentally friendly. Due to the trend towards sustainable production, vegetable oils have been used in research as an alternative to petroleum-based lubricants. The analysis of friction in sheet metal forming requires an appropriate tribotester simulating the friction conditions in a specific area of the sheet metal being formed. Research has used a special strip drawing tribometer, enabling the determination the value of the coefficient of friction in the blankholder zone in the deep drawing process. Quantitative analysis of the friction phenomenon is necessary at the stage of designing the technological process and selecting technological parameters, including blankholder pressure. This article presents the results of friction testing of 1.0338 (DC04) steel sheets using a strip drawing test. The experimental tests involved pulling a strip of sheet metal between two countersamples with a rounded surface. The tests were carried out on countersamples with different levels of roughness for the range of contact pressures occurring in the blankholder zone in the deep drawing process (1.7–5 MPa). The values of the coefficient of friction determined under dry friction conditions were compared with the results for edible (corn, sunflower and rapeseed) and non-edible (Moringa, Karanja) vegetable lubricants. The tested oils are the most commonly used vegetable-based biolubricants in metal forming operations. Multi-layer artificial neural networks were used to determine the relationship between the value of the contact pressure, the roughness of the countersamples, the oil viscosity and density, and the value of the coefficient of friction. Rapeseed oil provided the best lubrication efficiency during friction testing for all of the tested samples, with an average surface roughness of Sa 0.44–1.34 μm. At the same time, as the roughness of the countersamples increased, a decrease in lubrication efficiency was observed. The lowest root mean squared error value was observed for the MLP-4-8-1 network trained with the quasi-Newton algorithm. Most of the analysed networks with different architectures trained using the various algorithms showed that the kinematic viscosity of the oil was the most important aspect in assessing the friction of the sheets tested. The influence of kinematic viscosity on the value of the coefficient of friction is strongly dependent on the surface roughness of the countersamples.

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Section: Introductionmentioning

confidence: 99%

The Comparison of the Multi-Layer Artificial Neural Network Training Methods in Terms of the Predictive Quality of the Coefficient of Friction of 1.0338 (DC04) Steel Sheet

Trzepieciński

2024

Materials

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Computer-Integrated Surface Image Processing of Hydrogen-Saturated Steel Wear Products

Balitskii,

Kolesnikov,

Balitska

et al. 2024

Applied Sciences

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This paper briefly describes the conceptual direction of the application of computer vision (CV) methods that involve controlling the morphology of hydrogenated wear particles (WPs). During long-term operation, in the surface and subsurface layers of the materials of parts in the tribo-joint, changes in the micromechanisms of fracture occur, which change the morphology of WPs. It has been shown that the developed computer program (CP) can be used to monitor the fractography of the surface of wear particles, and, accordingly, it is possible to control changes in the surface morphology. Therefore, it is possible to predict the operational stability and durability of the tribo-joint. A conceptual scheme for determining the performance of a tribotechnical assembly depending on the determined parameters of WPs is presented. The modes marked on it, including normal operation, transient, run-in, and catastrophic failure, can be evaluated by robotics approaches.

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The Influence of Specific Features of Load and Hydrogen Charging on Steel Tribotechnical Properties

Cited by 2 publications

References 32 publications

The Comparison of the Multi-Layer Artificial Neural Network Training Methods in Terms of the Predictive Quality of the Coefficient of Friction of 1.0338 (DC04) Steel Sheet

The Comparison of the Multi-Layer Artificial Neural Network Training Methods in Terms of the Predictive Quality of the Coefficient of Friction of 1.0338 (DC04) Steel Sheet

Computer-Integrated Surface Image Processing of Hydrogen-Saturated Steel Wear Products

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