Optical coherence tomography (OCT) is an inline process monitoring technology for laser welding with various applications in the pre-, in-, and post-process. In-process monitoring with OCT focuses on the measurement of weld depth by the placement of a singular measurement beam into the keyhole. A laterally scanned measurement beam gives the opportunity to measure the keyhole and melt pool width. The processing region can be identified by separating higher signal intensities on the workpiece surface from lower signal intensities from the keyhole and the melt pool. In this work, we apply a scanned measurement beam for the identification of keyhole fluctuations. Different laser processing parameters are varied for laser welding of copper to evoke welds in the heat conduction regime, stable deep penetration welding, and unstable deep penetration welding. As keyhole instabilities can be related to the generation of spatter and other defects, we identified a feature for the classification of different weld statuses. In consequence, feedback can be given about possible defects which are originated in keyhole fluctuations (e.g., spatter).
In-process monitoring of weld penetration depth is possible with optical coherence tomography (OCT). The weld depth can be identified with OCT by statistical signal processing of the raw OCT signal and keyhole mapping. This approach is only applicable to stable welding processes and requires a time-consuming keyhole mapping to identify the optimal placement of a singular OCT measuring beam. In this work, we use an OCT measurement line for the identification of the weld depth. This approach shows the advantage that the calibration effort can be reduced as the measurement line requires only calibration in one dimension. As current literature focuses on weld depth measurement with a singular measurement point in the keyhole, no optimal algorithm exists for weld depth measurement with an OCT measurement line. We developed seven different weld depth processing pipelines and tested these algorithms under different weld conditions, such as stable deep penetration welding, unstable deep penetration welding, and heat conduction welding. We analyzed the accuracy of the weld depth processing algorithms by comparing the measured weld depth with metallographic weld depths. The intensity accumulation approach is identified as the most accurate algorithm for successful weld depth measurement with a scanning OCT measurement line.
The high demand for electronic products increases the need for high-quality welds of copper. Laser welding can be applied but may result in undesired weld characteristics such as humping or spatter. Process control is needed to identify defective welds in the production line. Surface topographical features can be used to identify different weld characteristics by optical coherence tomography (OCT). The resulting surface topography of a weld can be influenced by process parameters like its material properties or the application of process gas. In this work, we investigate the influence of different pure copper materials and process gas on weld seam surface features for the classification of quality-relevant weld characteristics. First, the resulting changes in weld depth and metallographic cross sections are qualitatively and quantitively characterized for different pure copper materials under the consideration of weld categories such as melt ejection, deep penetration welding, humping, and heat conduction welding with and without the application of shielding gas. Afterward, a qualitative and quantitative analysis of weld surface features is performed for the beforementioned categories under consideration of the copper material and shielding gas. As a result, an influence on the achievable weld depth could be identified for pure copper with residual phosphor content. No significant changes in surface topographical features could be identified for different material properties of copper. The influence of shielding gas and pure copper material is found to be negligible on surface topographical characteristics for process control.
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