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.

As a result of the rapidly growing importance of applications in electro mobility that require a precisely defined laser weld depth, the demand for inline process monitoring and control is increasing. To overcome the challenges in process data acquisition, this paper proposes the application of a novel sensor concept for deep penetration laser beam welding with high brilliance laser sources. The experiments show that optical coherence tomography (OCT) can be used to measure the weld depth by comparing the distance to the material surface with the distance to the keyhole bottom measured by the sensor. Within the presented work, the measuring principle was used for the first time to observe a welding process with a highly focused laser beam source. First, a preliminary experimental study was carried out to evaluate the influence of the angle of incidence, the material, and the weld joint geometry on the quality of the sensor signal. When using a multimode fiber laser with a focus diameter of 320 μm, the measurements showed a distinct behavior for aluminum and copper. The findings about the measurement signal properties were then applied to laser beam welding with a single-mode fiber laser with a spot diameter of only 55 μm. The spot diameter of the OCT measuring beam was about 50 μm and thus only slightly smaller than that of the single-mode processing beam. A wide variety of tests were carried out to determine the limits of the measurement procedure. The results show that the application of OCT allows inline monitoring of the weld depth using both a multimode and a highly focused single-mode laser beam. In addition, various influences on the signal were identified, e.g., the material-specific melt pool dynamics as well as several characteristic reflection and absorption properties.

Coaxial Laser Metal Deposition with wire (LMD-w) is a valuable complement to the already established Additive Manufacturing processes in production because it allows a direction-independent process with high deposition rates and high deposition accuracy. However, there is a lack of knowledge regarding the adjustment of the process parameters during process development to build defect-free parts. Therefore, in this work, a process development for coaxial LMD-w was conducted using an aluminum wire AlMg4,5MnZr and a stainless steel wire AISI 316L. At first, the boundaries for parameter combinations that led to a defect-free process were identified. The proportion between the process parameters energy per unit length and speed ratio proved crucial for a defect-free process. Then, the influence of the process parameters on the height and width of single beads for both materials was analyzed using a regression analysis. It was shown that linear models are suitable for describing the correlation between the process parameters and the dimensions of the beads. Lastly, a material-independent formula is presented to calculate the height increment per layer needed for an additive process. For future studies, the results of this work will be an aid for process development with different materials.

Remote laser beam welding significantly outperforms conventional joining techniques in terms of flexibility and productivity. This process benefits in particular from a highly focused laser radiation and thus from a well-defined heat input. The small spot sizes of high brilliance laser beam sources, however, require a highly dynamic and precise positioning of the beam. Also, the laser intensities typically applied in this context result in high process dynamics and in demand for a method to ensure a sufficient weld quality. A novel sensor concept for remote laser processing based on optical coherence tomography (OCT) was used for both quality assurance and edge tracking. The OCT sensor was integrated into a 3D scanner head equipped with an additional internal scanner to deflect the measuring beam independently of the processing beam. With this system, the surface topography of the process zone as well as the surrounding area can be recorded. Fundamental investigations on aluminum, copper, and galvanized steel were carried out. Initially, the influence of the material, the angle of incidence, the welding position within the scanning field, and the temperature on the OCT measuring signal were evaluated. Based on this, measuring strategies for edge tracking were developed and validated. It was shown that orthogonal measuring lines in the advance of the process zone can reliably track the edge of a fillet weld. By recording the topography in the trailing area of the process zone, it was possible to assess the weld seam quality. Comparing the results to microscopic measurements, it was shown that the system is capable of clearly identifying characteristic features of the weld seam. Also, it was possible to observe an influence of the welding process on the surface properties in the heat-affected zone, based on the quality of the measuring signal.

In an industrial environment, the quality assurance of weld seams requires extensive efforts. The most commonly used methods for that are expensive and time-consuming destructive tests, since quality assurance procedures are difficult to integrate into production processes. Beyond that, available test methods allow only the assessment of a very limited set of characteristics. They are either suitable for determining selected geometric features or for locating and evaluating internal seam defects. The presented work describes an evaluation methodology based on microfocus X-ray computed tomography scans (µCT scans) which enable the 3D characterization of weld seams, including internal defects such as cracks and pores. A 3D representation of the weld contour, i.e., the complete geometry of the joint area in the component with all quality-relevant geometric criteria, is an unprecedented novelty. Both the dimensions of the weld seam and internal defects can be revealed, quantified with a resolution down to a few micrometers and precisely assigned to the welded component. On the basis of the methodology developed within the framework of this study, the results of the scans performed on the alloy AA 2219 can be transferred to other aluminum alloys. In this way, the data evaluation framework can be used to obtain extensive reference data for the calibration and validation of inline process monitoring systems employing Deep Learning-based data processing in the scope of subsequent work.

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The automotive industry has a high demand for lightweight solutions for car body components to reduce the carbon dioxide emissions and to increase the range of electric cars. In this context, the joining methods play a significant role in enabling the lightweight construction. Specifically, the use of aluminum alloys for structural components or body panels poses a major challenge for joining technologies. These parts are often made from aluminum alloys AA6xxx, which are very susceptible to hot cracks during fusion welding. As laser beam welding is increasingly used for welding car body components, special techniques are required to avoid hot cracks in weld seams. Besides the use of filler wire, laser welding using an adapted intensity distribution is an innovative approach to get a defect-free weld seam coupled with a high surface quality. Due to the lack of flexible beam shaping optics for investigations on high power material processing using an adapted intensity distribution, a simulation method for this technique is presented. The impact of the adapted intensity on the process characteristics, e.g., the temperature field, the temperature gradients, or the molten pool geometry, can be determined by using this numerical model. The heat input by the adapted intensity distribution is composed of a stationary capillary geometry for the deep penetration welding process and an additional surface heat source. An experimental analysis was carried out to calibrate the simulation model. Using design of experiments, the weld seam geometry depending on the laser parameters can be predicted. Finally, the impact of an adapted intensity distribution on the geometry of the molten pool and the temperature field is shown.