The mechanical behaviour and texture evolution of lamellar Cu-Ag polycrystals are numerically investigated for a uniaxial compression test by three dimensional finite element simulations. In the representative volume element (RVE), the lamellar structure is generated inside the grains. A crystal plasticity material model for large deformations is used at each integration point. In this work, two cold drawn textured Cu-Ag polycrystals are modeled by periodic Voronoi tessellations in the finite element (FE) software ABAQUS. The FE calculations use periodic boundary conditions to simulate the mechanical behavior of the textured polycrystals. The numerical model is validated by experimental compression tests for a constant strain rate of 10 −4 s −1 at room temperature. The numerical results in terms of texture of each phase and the mechanical behavior have been compared with the experimental results.

Machine learning (ML) techniques are used to predict the coefficient of friction of an epoxy polymer resin (SU-8) and its composite coatings deposited on a silicon wafer. Filler type and the number of cycles are taken as the input parameters. The filler types included, two solid fillers namely, graphite and talc, and a liquid filler such as Perfluoropolyether (PFPE). Six variations of the SU8 coatings were developed based on the different combinations of filers used and tested. The experimental data generated for these different coatings for varying number of cycles (0 to 499) was used to train the different ML algorithms like ANN, SVM, CART, and RF to predict the coefficient of friction. The performance of these ML techniques was compared by calculating mean absolute error (MAE), root means square error (RMSE), and square of the correlation coefficient (R2). The ANN algorithm was observed to have the best (R2) metrics while the other ML techniques SVM, CART, and RF had a satisfactory performance with some inaccuracies seen for the CART algorithm for the data set under consideration.

This study presents a homogenization based on micromechanics approach for a two-phase copper (Cu)-silver (Ag) composite undergoing finite deformations. In this approach, the high-fidelity generalized method of cells (HFGMC) is implemented for the prediction of the effective behavior of two cold-drawn Cu-Ag composites with different drawing strains and to obtain the field (deformation gradient and stress) distributions in the composite. Both metals (Cu or Ag) are rate-dependent crystal plasticity material constituents. HFGMC is applied for studying the deformation behavior of two-phase Cu-Ag composites under uniaxial compression. The micromechanical approach has been verified by comparison with experimental and finite element simulation results. Results in terms of deformation behavior and field distributions are given for two different cold-drawn composites.

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