Numerical weld pool simulation for the accuracy improvement of inline weld depth measurement based on optical coherence tomography

Abstract

The aluminum alloy AA6082 is often used in high-voltage storage systems due to its favorable electrical and mechanical properties. Laser beam welding is a flexible process for producing the required welds. To protect the sensitive components of the battery cells, the required weld depth must be maintained in order to avoid destruction of the components and to reduce the number of defective parts. Optical coherence tomography (OCT) is a promising method for the inline monitoring of the deep welding process, since the depth of the vapor capillary can be measured without the influence of process emissions such as process light or welding fumes. Due to the robustness of the measurement method, the measured depth of the keyhole can be used as an input variable for a process control. However, there is a geometric difference between the optically detectable keyhole depth and the melt pool depth. This depth is additionally determined by fluid dynamic processes in the melt pool. In order to consider the difference between the melt pool depth and the keyhole depth when evaluating OCT measurement signals, a thermal simulation model was developed. By iteratively adapting the keyhole geometry in the simulation on the basis of genetic algorithms, an optimization of the correlation was achieved. The model was calibrated based on the comparison of metallographic cross sections with the calculated melt pool geometries. The results were used to provide a database for improving the performance of the signal processing algorithms. Based on the calculation results, it was possible to determine and quantify a dependence of the melt film thickness below the keyhole on the welding process parameters. By including the melt thickness below the keyhole, it was possible to increase the accuracy in the interpretation of OCT data with regard to the welding depth. The quantity can thus be used as an input quantity for signal processing algorithms for OCT data evaluation.