Machine learning-based quality prediction for laser cleaning of composite paint layers

Xingqiang Hou; Wei Cheng; Yuan Ren; Xinqiang Ma; Jingwen Wang; Jing Wang

doi:10.1117/12.3013351

29 November 2023 Machine learning-based quality prediction for laser cleaning of composite paint layers

Xingqiang Hou, Wei Cheng, Yuan Ren, Xinqiang Ma, Jingwen Wang, Jing Wang

Proceedings Volume 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023); 1293713 (2023) https://doi.org/10.1117/12.3013351
Event: International Conference on Internet of Things and Machine Learning (IoTML 2023), 2023, Singapore, Singapore

Abstract

In order to predict the laser cleaning quality of composite paint layer and realize the controllable paint removal of composite paint layer, this paper proposes a machine-learning based laser cleaning quality prediction method for composite paint layer. The aluminum alloy substrate surface is uniformly coated with 20 μm green epoxy primer and 43μmwhite polyurethane topcoat as experimental samples for laser cleaning experiments; based on the experimental data combined with the three machine learning algorithms (support vector machine SVR, BP neural network, and random forest RF) to establish a prediction model between the process parameters and the cleaning quality. The experimental results show that, compared with the RF model and BP neural network model, the SVR model is more accurate in predicting the quality of laser cleaning of composite paint layers, and the coefficient of determination of the prediction model is 0.961, the root mean square error is 1.738, and the average absolute error is 1.5162.This study obtains the prediction model of the quality of removing the paint thickness with high accuracy, realizes the effective prediction of the quality of the laser removing the paint for the It lays a model foundation for further research on intelligent control of laser cleaning of composite paint layers.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xingqiang Hou, Wei Cheng, Yuan Ren, Xinqiang Ma, Jingwen Wang, and Jing Wang "Machine learning-based quality prediction for laser cleaning of composite paint layers", Proc. SPIE 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023), 1293713 (29 November 2023); https://doi.org/10.1117/12.3013351

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KEYWORDS

Pulsed laser operation

Machine learning

Laser processing

Repetition frequency

Support vector machines

Statistical modeling

Surface roughness

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