CF / PEEK milling surface roughness modeling method based on L1-LSSVR algorithm

The CF/PEEK milling surface roughness model is established through the L1-LSSVR algorithm, which solves the problem that traditional methods are difficult to describe nonlinear characteristics, and realizes accurate modeling of the three-dimensional surface roughness of CF/PEEK material milling. It is suitable for high-end manufacturing fields such as aerospace, medical devices and automobiles.

CN120809007APending Publication Date: 2025-10-17FUZHOU UNIV ZHICHENG COLLEGE
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Patent Information

Application Number
CN202510918567.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-04
Publication Date
2025-10-17

AI Technical Summary

Technical Problem

Traditional methods are difficult to accurately describe the nonlinear characteristics of CF/PEEK material milling, resulting in complex surface roughness control and reduced machining performance.

Method used

The L1-LSSVR algorithm is used to learn from processing data, explore the complex relationship between process parameters and processing results, and establish an accurate surface roughness model. This includes static milling process parameter design, dynamic signal acquisition, feature extraction and feature selection. The Pearson correlation coefficient and L1 norm regularization are used for feature screening, and the multi-kernel function and Osprey optimization algorithm are combined to optimize the hyperparameters.

Benefits of technology

It achieves accurate modeling of the three-dimensional surface roughness of CF/PEEK material milling, improves the stability and accuracy of the processing results, and is suitable for key components in high-end manufacturing fields such as aerospace, medical devices and automobiles.

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Abstract

The invention provides a CF / PEEK milling surface roughness modeling method based on an L1-LSSVR algorithm. The method comprises the steps that S1, static milling process parameters serve as experimental variables, an experimental scheme is designed, dynamic signals in the milling process are collected in the experimental process, and after the experiment is finished, the CF / PEEK three-dimensional surface roughness is measured; s2, time domain features of the dynamic signals are extracted, and the time domain features are used for L1-LSSVR model training after being combined with static milling process parameters; s3, analyzing the correlation between the features through a Pearson's correlation coefficient, and eliminating redundant features with similar influence rules on the roughness of the three-dimensional surface; and performing feature selection by using L1 norm regularization, and screening out features having high correlation with the three-dimensional surface roughness. S4, establishing a CF / PEEK milling surface roughness model by taking the features screened in the step S3 as the input of an LSSVR algorithm and the CF / PEEK milling three-dimensional surface roughness as the output; according to the algorithm designed by the invention, modeling of the milling three-dimensional surface roughness of the CF / PEEK material can be effectively realized.
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