Basic Design Parameters Influencing on Axial Stiffness of the Spiral Wound Gasket

Tribological Properties of Nitrate Graphite Foils

This study investigates the tribological properties of graphite foils (GF) with densities of 1.0, 1.3, and 1.6 g/cm3, produced from purified natural graphite of different particle sizes (40-80 μm, 160-200 μm, >500 μm). Surface roughness was measured after cold rolling and friction testing at static (0.001 mm/s) and dynamic conditions (0.1 Hz and 1 Hz). Results showed that static friction tests yielded similar roughness values (Sa ≈ 0.5-0.7 μm, Sq ≈ 0.5-1.0 μm) across all densities and particle sizes. Dynamic friction tests revealed increased roughness (Sa from 0.7 to 3.5 μm, Sq from 1.0 to 6.0-7.0 μm). Friction coefficients (µ) decreased with higher sliding speeds, ranging from 0.22 to 0.13. GF with 40-80 μm particles had the lowest friction coefficient (µ = 0.13-0.15), while 160-200 μm particles had the highest (µ = 0.15-0.22). Density changes had minimal impact on friction for the 40-80 μm fraction but reduced friction for the 160-200 μm fraction. Young's modulus increased with density and decreased with particle size, showing values from 127-274 MPa for 40-80 μm, 104-212 MPa for 160-200 μm, and 82-184 MPa for >500 μm. The stress-strain state in the graphite foil samples was simulated under normal and tangential loads. This makes it possible to investigate the effect of the anisotropy of the material on the stress concentration inside the sample, as well as to estimate the elasticity modulus under normal compression. Structural analyses indicated greater plastic deformation in GF with 40-80 μm particles, reducing coherent-scattering region size from 28 nm to 24 nm. GF samples from 160-200 μm and >500 μm fractions showed similar changes, expanding with density increase from 18 nm to 22 nm. Misorientation angles of GF nanocrystallites decreased from 30° to 27° along the rolling direction (RD). The coherent scattering regions of GF with 40-80 μm particles increased, but no significant changes in the coherent scattering regions were observed for the 160-200 μm and >500 μm fractions during dynamic friction tests. Microstrains and residual macrostresses in GF increased with density for all fractions, expanding under higher friction-induced loads. Higher values of both stresses indicate a higher level of accumulated deformation, which appears to be an additional factor affecting the samples during friction testing. This is reflected in the correlation of the results with the roughness and friction coefficient data of the tested samples.

Multiscale Analysis of Composite Structures with Artificial Neural Network Support for Micromodel Stress Determination

Structures made of heterogeneous materials, such as composites, often require a multiscale approach when their behavior is simulated using the finite element method. By solving the boundary value problem of the macroscale model, for previously homogenized material properties, the resulting stress maps can be obtained. However, such stress results do not describe the actual behavior of the material and are often significantly different from the actual stresses in the heterogeneous microstructure. Finding high-accuracy stress results for such materials leads to time-consuming analyses in both scales. This paper focuses on the application of machine learning to multiscale analysis of structures made of composite materials, to substantially decrease the time of computations of such localization problems. The presented methodology was validated by a numerical example where a structure made of resin epoxy with randomly distributed short glass fibers was analyzed using a computational multiscale approach. Carefully prepared training data allowed artificial neural networks to learn relationships between two scales and significantly increased the efficiency of the multiscale approach.