Learning automata as a tool for machine learning, could search the optimal state adaptively in random environment. Function optimization is a fundamental issue and many practical models are ultimately the mathematical optimization problems. In this paper, we apply the basic continuous action-set reinforcement learning automata (CARLA) model to function optimization. An application model called equiCARLA is constructed by means of equidistant discretization and linear interpolation, and it presents a superiority over the existing algorithms not only in speed but also in precision. The experimental results demonstrate the effectiveness and efficiency of our model for function optimization.
Inside a scanning electron microscope (SEM) chamber, we performed an in situ interlaminar shear test on a z-pinned carbon fiber-reinforced aluminum matrix composite (Cf/Al) fabricated by the pressure the infiltration method to understand its failure mechanism. Experiments show that introducing a stainless-steel z-pin increases the interlaminar shear strength of Cf/Al composite by 148%. The increase in interlaminar shear strength is attributed to the high strength of the stainless-steel z-pin and the strong bonding between the z-pin and the matrix. When the z-pin/matrix interface failed, the z-pin can still experience large shear deformation, thereby enhancing delamination resistance. The failure mechanism of composite includes interfacial debonding, aluminum plough, z-pin shear deformation, frictional sliding, and fracture. These results in this study will help us understand the interlaminar strengthening mechanism of z-pins in the delamination of metal matrix composites.
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