This thesis investigates optical monitoring of laser-based additive manufacturing processes. The main focus is on spectral and Schlieren-based process monitoring for real-time defect detection and process optimization.

The research involves two new monitoring strategies for powder bed fusion - laser beam/metal, analysing energy absorption and process emissions in the first three papers. In paper A, the spectral signal of the laser-material interaction zone in powder bed fusion – laser beam/metal is analyzed using a coaxial and quasi-coaxial measurement setup. The study demonstrates that the detected spectral intensity distribution strongly depends on the angle of incidence between the measuring beam and the process zone. High-speed recordings and optical simulations enabled the development of a correction model for solid materials, which accounts for the numerical aperture of the measuring optics and laser intensity distribution across the working field. However, when measuring powders, strong signal fluctuations were observed, preventing a direct transfer of the correction model. This variation was attributed to differences in powder absorbance, which is further explored in paper B. This paper systematically investigates the absorbance behaviour of metal powders used in laser-based additive manufacturing. A high-precision spectrometer was used to measure 39 powders over a broad spectral range, examining the influence of aging, grain size, contamination and usage conditions. The study derives 20 technically relevant laser wavelengths, identifying those with improved process efficiency and stability. The resulting dataset provides a valuable foundation for laser parameter optimization by estimating the energy coupling efficiency for different materials. To enable in-situ absorbance determination in powder bed fusion - laser beam/metal, paper C introduces a method for high-resolution coaxial imaging of the powder bed at multiple wavelengths. This technique enables spatially resolved absorbance mapping across the entire processing plane, allowing for the detection of impurities, oxidation and foreign particles. The concept was experimentally validated using 20 different powders and further confirmed through optical simulations, ray tracing and comparative spectrometer measurements.

In three further papers, the laser material interaction and its effect on the refractive index variation of the gaseous process media is investigated for laser directed energy deposition. In paper D, a Schlieren imaging setup was applied to real-time monitoring of laser directed energy deposition, classifying different Schlieren phenomena and linking them to process instabilities and parameter deviations. A previously unknown recurring Schlieren structure was identified and its influence on coaxial imaging accuracy was analyzed using optical simulations that assigned a precise refractive index distribution to the observed Schlieren object. Paper E expands on these findings by implementing a background-oriented Schlieren system alongside shadowgraphy to analyze gas flow and refractive index variations from multiple orientations. A quantitative image processing method was developed to extract Schlieren intensity gradients and directional vectors, allowing for the correlation of Schlieren activity with process parameters. This approach enables the derivation of process boundaries based on optical flow measurements, offering a novel method for assessing process stability. Finally, paper F explores whether Schlieren-induced refractive index variations can be inferred from coaxial imaging data using an artificial-intelligence-based approach. A machine learning model was trained on background-oriented Schlieren data and coaxial imaging artifacts, demonstrating the feasibility of an indirect Schlieren analysis without requiring a dedicated Schlieren setup. By linking Schlieren structures to melt pool behaviour and process instabilities, the study contributes to real-time process monitoring and adaptive control strategies in laser directed energy deposition.

Overall, this thesis provides new insights into laser-material interactions, spectral absorbance properties and advanced optical process monitoring techniques. By combining spectroscopy, Schlieren imaging and artificial-intelligence driven analysis, this research advances the digitalization of laser-based additive manufacturing processes, improving process control, defect detection and manufacturing efficiency in powder bed fusion - laser beam/metal and laser directed energy deposition applications.