A data base was put together for the mechanical properties of microalloyed steels, which contained about 800 entries for ultimate tensile strength (UTS), yield strength (YS), and elongation. Using an evolutionary neural network, based upon a predator–prey genetic algorithms of bi‐objective type, this information was used to construct data‐driven models for UTS, YS, and elongation. The optimum Pareto tradeoffs between these properties were obtained using a multi‐objective genetic algorithm. The results led to some hitherto unexplored steel compositions with optimum properties. Some such steels were actually cast and the experimentally observed property values were found to be well in accord with the predicted results.
A dynamic model for a shell-type amplified piezoelectric actuator (APA) is proposed. The dimensions of the shell of a typical shell-type actuator allow one to model it as a set of connected beams. The contribution of the geometric nonlinearity in the axial direction of the beams due to bending is accounted for in the formulation. Subsequent nonlinear analysis using both analytical and numerical approaches reveals the presence of second harmonic in the response spectrum. This occurrence is also seen experimentally. A frequency regime is also identified within which the effect of quadratic nonlinearity is minimal and the actuator exhibits high fidelity. Dependence of the response spectrum on the actuator geometry has been studied. For a given forcing frequency, a certain geometric configuration is shown to exist, which maximizes the nonlinear effects. The effect of prestressing of the piezoelectric stack on the frequency spectrum is also studied. The obtained results are expected to lead to improved design of such actuators.
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