No abstract

No abstract

This work characterizes the stiffness of a finite domain containing one (biaxial ellipsoidal) void due to the combined effect of inclusion’s attributes: (1) size or volume fraction, VF, (2) shape or aspect ratio, AR, (3) angular orientation, and (4) location (position) within the matrix. The values and ranges of these ellipsoidal inclusion attributes are varied according to a matrix developed using design of experiments (DOE). Modified Mori–Tanaka method combined with dual-eigenstrain method (interior and exterior eigenstrain methods) is used to determine the effective stiffness tensor of the composite domain. Employing the numerically calculated normalized axial modulus [Formula: see text] values in SAS/STAT®, a nonlinear mathematical expression of [Formula: see text] as function of the void’s variables is arrived at Stiffness values found from the numerical homogenization scheme are experimentally corroborated using compression tests conducted on 3D-printed ABS cubes having a single ellipsoidal inclusion of various geometric attributes. In addition, finite element simulations were run of said uniaxial compression test cases to further validate the numerical homogenization results. Corroborated findings suggest that while the location of the inclusions in the matrix have no significant effect on normalized modulus [Formula: see text], the void’s volume fraction has the largest effect where it decreases with VF. The effect of the void’s orientation and elliptical aspect ratio are significant. [Formula: see text] increases with AR at angles ranging from 0–[Formula: see text]; at [Formula: see text][Formula: see text] are almost constant with AR, at angles of 60–[Formula: see text] values of [Formula: see text] decrease with AR. As AR approaches unity, the effect of orientation decreases significantly.

No abstract

In the past decade, data science became trendy and in-demand due to the necessity to capture, process, maintain, analyze and communicate data. Multiple regressions and artificial neural networks are both used for the analysis and handling of data. This work explores the use of meta-heuristic optimization to find optimal regression kernel for data fitting. It is shown that optimizing the regression kernel improve both the fitting and predictive ability of the regression. For instance, Tabu-search optimization is used to find the best least-squares regression kernel for different applications of buckling of straight columns and artificially generated data. Four independent parameters were used as input and a large pool of monomial search domain is initially considered. Different input parameters are also tested and the benefits of using of independent input parameters is shown.