To facilitate automation and intelligent scheduling of pivotal production line processes, a novel adaptive scheduling approach is introduced, leveraging fuzzy logic and reinforcement learning. It identifies key influencing factors— equipment status, raw material availability, order demands—as inputs for the fuzzy logic framework. By translating input values into fuzzy set membership degrees, it addresses production uncertainties. Reinforcement learning constructs an adaptive neural network model for scheduling, enacting actions guided by the strategy's generated instructions. Experimental outcomes demonstrate enhanced production efficiency, optimized resource utilization, cost reduction, and significant economic benefits for enterprises.
Aiming at the unbalanced vibration of rotor system, a novel integral squeeze film bearing damper is designed to control the vibration of rotor. In this paper, the stiffness and damping characteristics of tilting pad journal bearing and integral squeeze film bearing damper are calculated based on finite element method. At the same time, the rotor-support system model is established. The energy distribution of rotor system is analyzed, and the vibration control characteristics of integral squeeze film bearing damper are studied. The results indicate that, compared with tilting pad journal bearing, the novel integral squeeze film bearing damper can reduce the bending strain energy of the rotor by 36.5%. It can also reduce the vibration at the critical speed by 46.1%. In the spectrum diagram, it can be found that the amplitude of 1x frequency caused by the unbalanced fault has decreased by 45.2%. The research provides guidance for industrial application.
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