Optimization of Ultrasonic Prediction of TP2 Copper Grain Size Model Based on Machine Learning Algorithm

YUE Fengli; GUO Wei; LIU Minghua; CHEN Dayong; LIU Jinsong; LIU Huan; SONG Hongwu; WANG Songwei; CHU Xiaoguang

doi:10.3969/j.issn.1674-6457.2025.09.018

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Journal of Netshape Forming Engineering ›› 2025, Vol. 17 ›› Issue (9) : 185-194. DOI: 10.3969/j.issn.1674-6457.2025.09.018

Copper Alloy Forming

Optimization of Ultrasonic Prediction of TP2 Copper Grain Size Model Based on Machine Learning Algorithm

YUE Fengli^1a, GUO Wei^1a, LIU Minghua^1b, CHEN Dayong^2,*, LIU Jinsong^1b, LIU Huan^1a, SONG Hongwu², WANG Songwei³, CHU Xiaoguang³

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Abstract

The work aims to optimize the ultrasonic attenuation coefficient-average equivalent grain size model (AC-AGS) with the help of machine learning algorithms, so as to achieve efficient and accurate prediction of copper grain size, and provide key technical support in industrial production. The interface amplitude and the primary bottom echo amplitude of TP2 copper after heat treatment at different temperature were obtained by ultrasonic testing equipment, the attenuation coefficient was calculated by amplitude data, and the grain size of copper was obtained by the intercept method. In order to optimize the prediction model of copper grain size, three machine learning optimization algorithms, namely particle swarm optimization (PSO), genetic algorithm (GA) and differential evolution algorithm (DE), were introduced to optimize the grain size prediction model of TP2 copper. Results showed that the AC-AGS model coupled with the machine learning algorithm could realize the evaluation of the average grain size of precision copper. It was found that the PSO algorithm performed the best in the optimization results in this paper, and its coefficient of determination R² was 0.904 5, the Root Mean Square Error (RMSE) was 20.85, and the Mean Absolute Error (MAE) was 20.569. In conclusion, combined with the ultrasonic attenuation coefficient and the average grain size model of the machine learning optimization algorithm, the accurate prediction of the grain size of TP2 copper is realized. This model not only provides an efficient and accurate prediction tool for the non-destructive testing of copper grain size, but also provides new ideas and methods for the grain size prediction of other materials.

Key words

machine learning / ultrasonic testing / TP2 copper / grain size prediction / attenuation factor

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YUE Fengli, GUO Wei, LIU Minghua, CHEN Dayong, LIU Jinsong, LIU Huan, SONG Hongwu, WANG Songwei, CHU Xiaoguang. Optimization of Ultrasonic Prediction of TP2 Copper Grain Size Model Based on Machine Learning Algorithm[J]. Journal of Netshape Forming Engineering. 2025, 17(9): 185-194 https://doi.org/10.3969/j.issn.1674-6457.2025.09.018

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Funding

Key Project of Chongqing Natural Science Foundation Innovation and Development Joint Fund (CSTB2023NSCQ- LZX0116); Study on the Initiation Mechanism and Texture Evolution of the Cold-rolled Deformation Slip System of Zr-4 Plate (L1212410144015)