Dynamic optimization method for structural parameters of mechanical equipment parts based on digital twin

Abstract

The invention discloses a dynamic optimization method for structural parameters of mechanical equipment parts based on digital twin. The method comprises the following steps: realizing digital mirrorimages of equipment corresponding to physical space in virtual space by constructing a high-fidelity model, which is convenient for later structural parameters modification and super realistic simulation; dynamically and synchronously reflecting the state of an entity corresponding to the physical space in real time in the virtual space by performing super realistic simulation, and realizing realistic motion of the real condition of the physical equipment in the virtual space; and meanwhile, constructing a neural network structure by utilizing a deep learning theory, establishing a relation between the structural parameters and the fatigue life with the help of strong digital mining and mapping capabilities of the neural network structure, and realizing the dynamic optimization of the structural parameters by combining the high-fidelity model and the super realistic simulation environment. According to the invention, dynamic optimization and reverse guidance of the virtual space to thestructural parameters of the physical space are realized, and optimization efficiency and authenticity are improved.

Description

technical field [0001] The invention relates to the field of deep learning, in particular to a method for dynamic optimization of structural parameters of mechanical equipment parts based on digital twins. Background technique [0002] Digital twins provide important theoretical basis and technical support for the real-time interaction and two-way connection between virtual space and physical space. In recent years, they have achieved rapid development in both theory and application. At present, digital twin technology is applied to aerospace equipment and workshop production control. For large-scale equipment such as general-purpose machinery with complex working environments and changing conditions, the optimization methods for the structural parameters of key components are backward, basically staying in empirical design and parameter static simulation design, and it is impossible to conduct high-fidelity simulations of actual operating conditions. This leads to poor acc...

AI Technical Summary

Problems solved by technology

The optimization methods of structural parameters of existing mechanical equipment parts are backward, the optimization efficiency is low, and the authenticity needs to be improved. It is difficult to optimize the key structural parameters of parts under complex working conditions

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