Spectrometer as a quantitative sensor for predicting the weld depth in laser welding

Abstract

Spectrometers have been used for quality assessment or as auxiliary sensors in multisensor systems to record emissions at different wavelengths. This study developed a single-spectrometer sensor to quantitatively predict the laser penetration depth in Cu/Al overlap and dual-phase (DP) steel bead-on-plate welding joints. The spectrometer signals were sampled at 100 Hz, and an optical coherence tomography (OCT) sensor was used to estimate the in situ penetration depth. A one-dimensional (1D) convolution neural network (CNN) model was developed using the current step of the spectrometer signal. Furthermore, a two-dimensional (2D) CNN model was developed using 10 recent steps of the spectrometer signals. Consequently, 1732 and 3000 data points for DP steel and Al/Cu overlap welding were used for the penetration-depth modeling. Through model training, mean absolute errors of 22.67 and 52.96 µm were achieved for the Al/Cu overlap and DP steel welding, respectively, corresponding to approximately 1.5 % of the substrate thickness. The 2D-CNN models outperformed the 1D-CNN models for both material combinations. However, a slight delay was observed in the response of the models when confronted with abrupt output changes. This study confirmed that spectroscopy can serve as the sole quantitative sensing method for laser welding monitoring and control.