He Xiaoyong, Hu Jianchang, Peng Xiao, Song Jun, Yuan Yufeng, Qu Junle
School of Electronic Engineering and Intelligentization, Dongguan University of Technology, Dongguan, Guangdong, 523808, China.
School of Electronic Engineering and Intelligentization, Dongguan University of Technology, Dongguan, Guangdong, 523808, China; State Key Laboratory of Radio Frequency Heterogeneous Integration Shenzhen University, College of Physics and Optoelectronic Engineering, Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Shenzhen University, Shenzhen, Guangdong, 518060, China.
Anal Chim Acta. 2024 Nov 22;1330:343271. doi: 10.1016/j.aca.2024.343271. Epub 2024 Sep 25.
Owing to its excellent machinability and less toxicity, bismuth brass has been widely used in manufacturing various industrial products. Thus, it is of significance to perform rapid and accurate identification of bismuth brass to reveal the alloying properties. However, the analytical lines of various elements in bismuth brass alloy products based on conventional laser-induced breakdown spectroscopy (LIBS) are usually weak. Moreover, the analytical lines of various elements are often overlaped, seriously interfering with the identification of bismuth brass alloys. To address these challenges, developing an advanced strategy enabling to achieve ultra-high accuracy identification of bismuth brass alloys is highly desirable.
This work proposed a novel method for rapidly and accurately identifying bismuth brass samples using deep learning assisted femtosecond laser-ablation spark-induced breakdown spectroscopy (fs-LA-SIBS). With the help of fs-LA-SIBS, a spectral database containing high quality LIBS spectra on element components were constructed. Then, one-dimensional convolutional neural network (CNN) was introduced to distinguish five species of bismuth brass alloy. Amazingly, the optimal CNN model can provide an identification accuracy of 100 % for specie identification. To figure out the spectral features, we proposed a novel approach named "segmented fs-LA-SIBS wavelength". The identification contribution from various wavelength intervals were extracted by optimal CNN model. It clearly showed that, the differences of spectra feature in the wavelength interval from 336.05 to 364.66 nm can produce the largest identification contribution for an identification accuracy of 100 %. More importantly, the feature differences in the four elements such as Ni, Cu, Sn, and Zn, were verified to mostly contribute to identification accuracy of 100 %.
To the best of our knowledge, it is the first study on one-dimensional CNN configuration assisted with fs-LA-SIBS successfully employed for performing identification of bismuth brass. Compared with conventional machine learning methods, CNN has shown significant more superiority. To reveal the tiny spectra differences, the classification contribution from spectra features were accurately defined by our proposed "segmented fs-LA-SIBS wavelength" method. It can be expected that, CNN assisted with fs-LA-SIBS has great promising for identifying the differences from various element components in metallurgical field.
由于铋黄铜具有出色的可加工性和较低的毒性，已被广泛用于制造各种工业产品。因此，快速准确地识别铋黄铜以揭示其合金特性具有重要意义。然而，基于传统激光诱导击穿光谱（LIBS）的铋黄铜合金产品中各种元素的分析谱线通常较弱。此外，各种元素的分析谱线经常重叠，严重干扰了铋黄铜合金的识别。为应对这些挑战，迫切需要开发一种能够实现铋黄铜合金超高精度识别的先进策略。
本工作提出了一种利用深度学习辅助飞秒激光烧蚀火花诱导击穿光谱（fs-LA-SIBS）快速准确识别铋黄铜样品的新方法。借助fs-LA-SIBS，构建了一个包含元素成分高质量LIBS光谱的光谱数据库。然后，引入一维卷积神经网络（CNN）来区分五种铋黄铜合金。令人惊讶的是，最优的CNN模型在种类识别方面可提供100%的识别准确率。为了找出光谱特征，我们提出了一种名为“分段fs-LA-SIBS波长”的新方法。通过最优的CNN模型提取了不同波长区间对识别的贡献。结果清楚地表明，波长区间在336.05至364.66nm之间的光谱特征差异对100%的识别准确率贡献最大。更重要的是，镍、铜、锡和锌这四种元素的特征差异被证实对100%的识别准确率贡献最大。
据我们所知，这是首次关于一维CNN配置辅助fs-LA-SIBS成功用于铋黄铜识别的研究。与传统机器学习方法相比,CNN已显示出明显的优势。为了揭示微小的光谱差异，我们提出的“分段fs-LA-SIBS波长”方法准确地定义了光谱特征的分类贡献。可以预期，CNN辅助fs-LA-SIBS在识别冶金领域各种元素成分差异方面具有巨大潜力。
