Real-time prediction of quality characteristics in laser beam welding using optical coherence tomography and machine learning

Abstract

Laser beam welding significantly outperforms conventional joining techniques in terms of flexibility and productivity. The process benefits, in particular, from the highly focused energy and thus from a well-defined heat input. The high intensities of brilliant laser radiation, however, induce very dynamic effects and complex processes within the interaction zone. The high process dynamics require a consistent and reliable quality assurance to ensure the required weld quality. A novel sensor concept for laser material processing based on optical coherence tomography (OCT) was used to measure the capillary depth of the keyhole during deep penetration welding. The OCT measurements were compared with analyses of the surface quality of the weld seams. A machine learning approach could be utilized to reveal correlations between the weld depth signal and the weld seam surface quality, underlining the high level of information contained in the OCT signal about characteristic process phenomena that affect the weld seam quality. Fundamental investigations on aluminum, copper, and galvanized steel were carried out to analyze the structure of the data recorded by the OCT sensor. Based on that, evaluation strategies focusing on quality characteristics were developed and validated to enable a valid interpretation of the OCT signal. The topography of the weld seams was used to classify the surface quality and correlated with the weld depth signal of the OCT system. For this purpose, a preprocessing of the OCT data and a detailed analysis of the topographic information were developed. The processed data were correlated using artificial neural networks. It was shown that by using adequate network structures and training methods, the inline process data of the capillary depth can be used to predict the surface quality with decent prediction accuracy.

A Novel Approach to the Holistic 3D Characterization of Weld Seams—Paving the Way for Deep Learning-Based Process Monitoring

“…In addition to evaluating the capillary depth, the OCT signal can be utilized to assess correlations between the course of the weld depth and the process stability. The hypothesis that the course of the measured capillary depth correlates with the process stability during laser beam welding could already be confirmed in [22]. Therefore, in further investigations, it can be evaluated analogously to what extent a correlation between mutations in the OCT signal and the distribution of process pores can be found.…”

A Novel Approach to the Holistic 3D Characterization of Weld Seams – Paving the Way for Deep Learning-Based Process Monitoring

“…It could be demonstrated that the interferometric measuring principle, applied coaxially to the laser beam, enables a measurement of the capillary depth during deep penetration welding [21]. In addition to the determination of the penetration depth, the quality of the weld seam surface concerning the occurrence of irregularities in the topography could be determined based on the measured capillary depth [22].…”

Weld Classification With Feature Extraction by FRESH Algorithm Based on Surface Topographical Optical Coherence Tomography Data for Laser Welding of Copper

“…Optical coherence tomography (OCT) is an interferometric measurement technology that enables the inline measurement of weld seam topography coaxially to the processing laser in a scanning optic [7][8][9][10][11]. For instance, Hartung et al [11] combined an OCT with external highspeed video camera measurements for the identification of spatters which represent height deposits on the workpiece surface.…”