Abstract：
In this study, eight different painted stainless steel 304L specimens were laser-cleaned using different process parameters, such as laser power, scan speed, and the number of repetitions. Laser-induced breakdown spectroscopy (LIBS) was adopted as the monitoring tool for laser cleaning. Identification of LIBS spectra with similar chemical compositions is challenging. A convolutional neural network (CNN)-based deep learning method was developed for accurate and rapid analysis of LIBS spectra. By applying the LIBS-coupled CNN method, the classification CNN model accuracy of laser-cleaned specimens was 94.55%. Moreover, the LIBS spectrum analysis time was 0.09 s. The results verified the possibility of using the LIBS-coupled CNN method as an in-line tool for the laser cleaning process.
摘要：
在这项研究中,使用不同的工艺参数对八个不同的涂漆不锈钢304L试样进行了激光清洗，如激光功率，扫描速度,以及重复的次数。采用激光诱导击穿光谱（LIBS）作为激光清洗的监测工具。具有相似化学组成的LIBS光谱的鉴定是具有挑战性的。开发了一种基于卷积神经网络（CNN）的深度学习方法，用于准确，快速地分析LIBS光谱。通过应用LIBS耦合CNN方法，激光清洗标本的CNN模型分类准确率为94.55%。此外，LIBS光谱分析时间为0.09s。结果验证了使用LIBS耦合CNN方法作为激光清洁过程的在线工具的可能性。
