Finite element model and neural network model-based variable parameter laser ablation effect rapid prediction method

By combining finite element model and neural network model, the limitations of existing technologies in predicting laser ablation effects in terms of applicable scenarios and material range are solved, achieving high-precision and rapid prediction in dynamic scenarios, applicable to a variety of materials and complex environments.

CN122347002APending Publication Date: 2026-07-07NORTHWEST INST OF NUCLEAR TECH
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Patent Information

Application Number
CN202610341130.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-03-19
Publication Date
2026-07-07

AI Technical Summary

Technical Problem

Existing technologies have limited applicability in predicting laser ablation effects, a limited range of materials, and insufficient real-time performance, making it difficult to adapt to dynamic application requirements involving dynamic changes in laser parameters and complex environments.

Method used

A rapid prediction method for the variable parameter laser ablation effect based on finite element model and neural network model is adopted. By constructing a finite element model of the laser ablation effect, obtaining simulation data and training the neural network model, and combining it with actual working condition parameters, a real-time prediction is made.

Benefits of technology

It achieves high-precision and fast prediction of laser ablation effects in highly dynamic scenarios, has a wide range of applications, and can modify the laser irradiation time and calculation time step in real time to improve the accuracy of prediction results.

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Abstract

The invention discloses a variable parameter laser ablation effect rapid prediction method based on a finite element model and a neural network model. The problems that in the prior art, the applicable scene is limited, or the applicable material range is limited, and the real-time performance is insufficient are solved. Simulation data (namely a second data set) is obtained through a laser ablation effect finite element model, neural network model training is trained and tested through the simulation data, an optimal neural network model is obtained, corresponding data of a to-be-tested material is obtained according to actual working conditions, and the calculation time step length and the laser irradiation time are set, so that the optimal neural network model is obtained. The optimal neural network model is combined, the laser ablation effect is predicted in real time, the calculation precision is high, consumed time is short, and the method is suitable for rapid prediction of the laser ablation effect in a strong dynamic scene.
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