激光清洗铝合金表面漆层的实验与工艺参数优化研究
Experimental and process parameter optimization study of laser cleaning of aluminum alloy surface paint layers
顾志同 1王涛 1冯伟峰 1张鑫 1姚涛1
作者信息
- 1. 河北工业大学机械工程学院,天津 300132
- 折叠
摘要
采用了纳秒脉冲激光对7050铝合金表面的丙烯酸聚氨酯漆进行了激光清洗,研究了激光功率、扫描速度和重复频率对除漆率和表面粗糙度的影响.通过对基体表面的超景深图像进行二值化处理,实现了对除漆率的定量分析.结果表明,随着激光功率的增大,除漆率逐步提高,表面粗糙度先降低后升高.随着扫描速度和重复频率的增大,除漆率先升高后降低,表面粗糙度先降低后升高.采用GRNN(generalized regression neural network)神经网络模型建立了激光工艺参数与清洗质量之间的关联密度函数,通过MOSSA(multi-objective sparrow search algorithm)算法对模型进行多目标优化,得到了最佳的激光除漆工艺参数组合.在该激光工艺参数下,除漆率为99.16％,表面粗糙度为1.32 μm.
Abstract
In this paper,a nanosecond pulsed laser was used for laser cleaning of acrylic urethane paint on the surface of 7050 aluminum alloy,and the effects of laser power,scanning speed and repetition frequency on the paint removal rate and surface roughness were investigated.Quantitative analysis of the paint removal rate was achieved by binarizing the super depth of field image of the substrate surface.The results show that as the laser power increases,the paint removal rate gradually increases and the surface roughness first decreases and then increases.As the scanning speed and repetition frequency increase,the paint removal rate increases and then decreases,and the surface roughness decreases and then increases.A generalized regression neural network(GRNN)model was used to establish the correlation density function between laser process parameters and cleaning quality.The best combination of parameters for the laser paint removal process was obtained by multi-objective optimization of the model through the multi-objective sparrow search algorithm(MOSSA).With this laser process parameter,the paint removal rate was 99.16％and the surface roughness was 1.32 pm.
关键词
激光清洗/激光除漆/神经网络/优化算法Key words
laser cleaning/laser paint removal/neural networks/optimization algorithms引用本文复制引用
出版年
2025
NETL
NSTL
万方数据