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%.
Highlights
The advent of Industry 4.0 has brought significant enhancements to manufacturing processes, which are based on smart and autonomous systems, and are incorporated with data and machine learning (ML) [1]
This study presents a deep neural network (DNN)-based quality assessment method in spectrometry-based laser beam welding (LBW)
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Summary
The advent of Industry 4.0 has brought significant enhancements to manufacturing processes, which are based on smart and autonomous systems, and are incorporated with data and machine learning (ML) [1]. Rizzi et al [20] investigated the spectroscopic signals, produced by the laser-induced plasma optical emission, together with energetic and metallographic analyses of CO2 laser-welded stainless-steel lap joint, using the response surface methodology (RSM) This statistical approach allowed the study of the influence of the laser beam power and laser welding speed, on the plasma plume electron temperature, joint penetration depth, and melted area. Konuk et al [21] used a spectrometer to collect the optical emissions of the welding area, and calculate the electron temperature, and the data measured and calculated were used to determine the weld quality and to control the laser power. Sebestova et al [22] designed a sensor to monitor the pulsed Nd:YAG laser welding process, based on the measurement of the plasma electron temperature, and this sensor was used to detect the weld penetration depth.
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