**Jiyoung Yu 1, Huijun Lee 1, Dong-Yoon Kim 2, Munjin Kang 2,\* and Insung Hwang 2,\***


Received: 9 June 2020; Accepted: 23 June 2020; Published: 24 June 2020

**Abstract:** For the automation of a laser beam welding (LBW) process, the weld quality must be monitored without destructive testing, and the quality must be assessed. A deep neural network (DNN)-based quality assessment method in spectrometry-based LBW is presented in this study. A spectrometer with a response range of 225–975 nm is designed and fabricated to measure and analyze the light reflected from the welding area in the LBW process. The weld quality is classified through welding experiments, and the spectral data are thus analyzed using the spectrometer, according to the welding conditions and weld quality classes. The measured data are converted to RGB (red, green, blue) values to obtain standardized and simplified spectral data. The weld quality prediction model is designed based on DNN, and the DNN model is trained using the experimental data. It is seen that the developed model has a weld-quality prediction accuracy of approximately 90%.

**Keywords:** deep neural network; high strength steel; laser beam welding; penetration; quality assessment; spectrometer
