Rubber friction on dry surfaces consists of adhesive and hysteresis friction. Adhesive friction originates from interfacial forces tangential to the contact surface, while hysteresis friction originates from the asymmetry of the viscoelastic force normal to the contact surface. Although hysteresis friction is typically calculated by converting energy dissipation into force, it can also be directly computed as a force based on its original definition. This article introduces several studies on rubber friction using the viscoelastic foundation model, which calculates hysteresis friction between the viscoelastic solid and a rigid probe. This model enables analyses of the effects of viscoelasticity and contact conditions on rubber friction characteristics. Additionally, an experimental approach for friction component separation, based on hysteresis friction theory derived from the viscoelastic foundation model, is discussed.

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Rubber, as a representative soft material, exhibits frictional behavior that is governed by both adhesive and hysteresis mechanisms, and is highly sensitive to velocity, load, temperature, and contact history. Unlike friction in rigid materials, the response of rubber is inherently nonlinear and time-dependent due to its viscoelastic nature. While traditionally regarded as unpredictable, recent studies have shown that rubber's viscoelasticity and deformability can be strategically leveraged for functional friction design. This paper reviews advances in surface patterning (e.g., grooves, hierarchical textures, bio-inspired structures) and internal architecture (e.g., stiffness gradients, embedded structures), which enable directional control of friction, even under lubrication. It also highlights key phenomena such as Schallamach waves and history-dependent effects, and introduces emerging modeling approaches, including metainterface design, which redefine friction as a tunable and designable function in soft systems.

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Failure in paper feeding such as double feeding or paper jams are one of the main problems that MFP (Multi Function Peripheral) manufacturers have to deal with, as they are a complex process consisting of multiple factors. For this paper feeding system, which functions properly with proper friction between the paper and the rubber rollers, the fact that users are using many different brands of printing paper on the market means that this problem is made more complicated. One of the main causes of the failure is paper lint. Paper lint mainly include fractions of the cellulose fibers, paper filler particles and dusts. Those paper lint or dusts are generated through the paper feed and adhere to surfaces of rubber rollers. or fly as dust inside of printers. This paper describes a method for visualizing paper lint adhesion and a simple evaluation method for paper lint adhesion performance.

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Soft material such as rubber is widely used in products that require slip resistance and sealability, including tires, shoes, and mechanical seals. An increase in the friction of tread rubber in lubricants is crucial for improving safety in our daily lives. However, the friction of tread rubber in lubricants is influenced by various parameters such as tread geometry and material properties. This friction behavior is primarily governed by the fluid pressure. When fluid flows through the expansion gap formed at the interface, negative fluid pressure is generated because of wedge action. This negative fluid pressure acts as an adsorption force on the tread rubber, reducing fluid-film thickness. As a result, adhesion friction between the tread rubber and the mating surface increases, along with the viscous resistance of the fluid, leading to higher overall friction. One technical approach to enhancing friction in lubricated conditions is controlling fluid pressure through inclined-groove tread patterns. This design can effectively increase friction by optimizing hydrodynamics at the interface.

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We introduce ultra-high-speed friction of soft matter. When polymer gel, a constituent of soft matter, is slid against a rigid indenter at a sliding speed above its elastic wave (Rayleigh or Secondary wave) speeds, the contact pressure distribution and friction coefficient change drastically, leading to a shock-induced fracture of the gel. In contrast, a considerable reduction in the friction coefficient is observed at the sliding speed slightly smaller than the wave speeds. We discuss the mechanisms for such abnormal friction and the application to other engineering problems.

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Two-dimensional materials (2D materials) are materials that consist of a single layer (or just a few atomic layers) of atoms, making it effectively two-dimensional in structure. Compared to bulk materials, 2D materials are extremely thin, have a large surface area, and exhibit unique electrical, optical, and mechanical properties. This study demonstrates the efficient synthesis of high-quality, oriented hBN nanosheets on nickel substrates using the Sputtering Annealing Hybrid Process (SAHP). The aim is to enable the synthesis of unexplored nanomaterials through van der Waals heterostructures (vdWH), using graphene, hBN, and other 2D materials as building blocks. This approach also seeks to address the technical challenges encountered in device development.

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The dimensionless material parameter in the Dowson-Higginson oil film thickness equation includes the pressure-viscosity coefficient α of the lubricant. It is known that α depends on pressure and temperature, but a simple relational equation between pressure and temperature for α has not yet been obtained. On the other hand, in recent years, various analyses using statistical methods such as machine learning have become widespread, making it possible to predict physical properties without a clear theory. In this study, we investigated the possibility of estimating α using third-order polynomial functions for pressure and temperature variables, respectively. The unknown 7 parameters values are determined by a statistical regression method of the polynomial functions to experimentally measured pressure-temperature-viscosity data. Furthermore, this prediction equation can be easily calculated automatically using regression analysis in Excel and is highly effective due to the small standard deviation of its error. As described above, we were able to verify that α can be predicted by statistical analysis predicted by statistically obtained equations.

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