WOA-LSSVM-based roughness prediction method for 50mm thick Q235 carbon steel 40kW laser bevel cutting

Tianhao Li; Wei Cheng; Zifa Xu; Lizhi Su; Wenwen Yang

doi:10.1117/12.3052608

12 December 2024 WOA-LSSVM-based roughness prediction method for 50mm thick Q235 carbon steel 40kW laser bevel cutting

Tianhao Li, Wei Cheng, Zifa Xu, Lizhi Su, Wenwen Yang

Proceedings Volume 13446, Sixth International Conference on Optoelectronic Science and Materials (ICOSM 2024); 134461G (2024) https://doi.org/10.1117/12.3052608
Event: 6th International Conference on Optoelectronic Science and Materials (ICOSM 2024), 2024, Hefei, China

Abstract

This study proposes a high-power laser bevel cutting roughness prediction method based on the whale algorithm optimized least squares support vector machine. The 40kW laser bevel cutting system is used to carry out a 30°V-bevel cutting test on 50mm-thick Q235 carbon steel; based on the results of the orthogonal test, a regression prediction model between the laser bevel cutting process parameters and the roughness of the bevel cut surface is established by the least-squares support vector machine; the whale optimization algorithm is used to realize the optimization of the penalty parameter and the kernel parameter in the model of the least-squares support vector machine; the optimized model is used to predict the roughness of the bevel cut surface. The optimized model is used to predict the roughness of bevel cut. The experimental results show that compared with BP neural network, RBF neural network, least squares support vector machine and particle swarm optimization least squares support vector machine model, this model is more accurate in predicting the roughness of bevel cut, and the coefficient of determination of this prediction model is 0.9576, the root mean square error is 0.0326, and the mean bias error is 0.0409.This study can get the prediction of bevel cut roughness with high accuracy, and it can be used to predict the roughness of bevel cut. This study can get the bevel cutting roughness prediction model with high accuracy and realize the effective prediction of high power laser bevel cutting roughness.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Tianhao Li, Wei Cheng, Zifa Xu, Lizhi Su, and Wenwen Yang "WOA-LSSVM-based roughness prediction method for 50mm thick Q235 carbon steel 40kW laser bevel cutting", Proc. SPIE 13446, Sixth International Conference on Optoelectronic Science and Materials (ICOSM 2024), 134461G (12 December 2024); https://doi.org/10.1117/12.3052608

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KEYWORDS

Laser cutting

Mathematical optimization

Data modeling

Support vector machines

High power lasers

Carbon

Fiber lasers

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