Process monitoring is becoming increasingly important in laser-based manufacturing and is of particular importance in the field of additive manufacturing [e.g. Laser Powder Bed Fusion (LPBF)]. Process monitoring enables a reduction of production costs and a lower time-to-market. Furthermore, the data can be used to create a digital twin of the workpiece. There are already many established processing head-integrated monitoring systems for such applications as the multispectral analysis of process radiation. However, the monitoring of complex signals in systems with F-Theta scanner lenses is very challenging and requires specially adapted optics or measuring sensors.

In this paper a potential arrangement for spectroscopy-based process monitoring in pre-objective scanning is presented. The process radiation was monitored using a coaxial and a quasi-coaxial observation system. The measurements were carried out on both a solid and a powder coated sample of 2.4668 (Inconel 718) to show the potential use of these systems in laser-based additive manufacturing. In order to obtain comprehensive data about the process signal, the process zone was analyzed at different angles of incidence (AOI) of the laser using a high-speed camera (HSI) and a spectrometer. The connection between the HSI and the spectral measurements is discussed. The ionization of the material and the formation of a plasma was observed and found to lose intensity as the angle of incidence increases. A model of the system that demonstrates the intensity of the emitted radiation of the plasma was created. It enables the measured values to be corrected. The corrected measurement data can be used to detect impurities or a non-ideal energy input across the entire processing field, which is a move towards robust process monitoring.

Absorbance is often used for simulations or validation of process parameters for powder-based laser materials processing. In this work, the absorbance of 39 metal powders for additive manufacturing is determined at 20 laser wavelengths. Different grain sizes and aging states for: steels, aluminum alloys, titanium alloys, Nitinol, high entropy alloy, chromium, copper, brass and iron ore were analyzed. For this purpose, the absorbance spectrum of the powders was determined via a dual-beam spectrometer in the range of λ = 330 - 1560 nm. At the laser wavelengths of λ = 450 nm, 633 nm and 650 nm, the absorbance averaged over all materials was found to increase by a factor of 2.4 up to 3.3 compared to the usual wavelength of λ = 1070 nm, with minimal variations in absorbance between materials. In the investigation of the aged or used powders, a loss of absorbance was detectable. Almost no changes from the point of view of processing aged and new AlSi10Mg powders, is expected for laser sources with λ = 450 nm. The resulting measurements provide a good basis for process parameters for a variety of laser wavelengths and materials, as well as a data set for improved absorbance simulations.

This study presents an approach for in-situ monitoring of laser powder bed fusion. Using standard laser optics, coaxial high-resolution multispectral images of powder beds are acquired in a pre-objective scanning configuration. A continuous overview image of the entire 114 × 114 mm powder bed can be generated, detecting objects down to 20 µm in diameter with a maximum offset of 22–49 µm. Multispectral information is obtained by capturing images at 6 different wavelengths from 405 nm to 850 nm. This allows in-line determination of the absorbance of the powder bed with a maximum deviation of 2.5% compared to the absorbance spectra of established methods. For the qualification of this method, ray tracing simulations on powder surfaces and tests with 20 different powders have been carried out. These included different particle sizes, aged and oxidized powders.

A schlieren system, adapted for Laser Directed Energy Deposition, was used to monitor and analyze the process zone under various conditions, including deliberate contamination and parameter limits. This approach enabled the identification and correlation of process-induced defects with schlieren phenomena. Events and zones were characterized and qualitative categorized to validate schlieren monitoring as a diagnostic tool. Notably, a highly active and spatially confined schlieren formation was consistently observed above the melt pool. Using a tailored schlieren optical setup and simulations, schlieren patterns were linked to refractive index changes in process gases, enabling quantitative analysis. The refractive index within the hot gas dome over the molten pool was observed to range from 1.00000712 to 1.00875126, with fluctuation speeds reaching up to 210 m/s. As a result, a model was developed to describe the impact of refractive index dynamics on the performance of coaxial monitoring systems in laser processes. A case study using an exemplary imaging monitoring system demonstrated that schlieren phenomena can cause wavelength-dependent lateral geometric shifts of up to 228 µm, significantly affecting the accuracy of object-based monitoring outcomes. The findings offer critical insights into the complex interplay between refractive index variations and monitoring results, paving the way for refined monitoring strategies that enhance reliability and precision in laser cladding applications.

Schlieren monitoring techniques were applied to Laser Directed Energy Deposition (L-DED) and Laser Cladding (LC) processes to evaluate their performance in capturing thermal dynamics and spatter formation under varying process parameters. Two schlieren setups were compared based on their ability to detect changes in temperature, pressure and material state within the process zone. The first, using a background-oriented design, exhibited higher resolution and sensitivity to local thermal gradients and plume dynamics, while the second, with a broader field of view, demonstrated enhanced stability in monitoring cumulative thermal buildup. Optical flow analysis revealed a strong correlation between line energy and the magnitude of optical turbulence, particularly in regions where vaporization occurred, with a clear plateau observed between 110.6 J/mm and 243.9 J/mm, corresponding to optimal melt pool conditions. Beyond 243.9 J/mm, a significant increase in optical flow was observed, indicating plasma formation and enhanced turbulence. A dome-like schlieren structure consistently formed above the melt pool, expanding with higher energy input, offering insights into the balance between thermal buoyancy and vapor pressure. Additionally, the quadratic relationship between line energy and the schlieren dome volume of enclosed optical flow provided a means to identify energy-efficient and stable process conditions. The findings underscore the potential of schlieren-based monitoring for precise control and optimization of additive manufacturing processes, with implications for improving process stability and minimizing defects like spatter and porosity.

Real-time process monitoring is essential for ensuring stability and defect control in directed energy deposition - laser beam/metal (DED-LB/M). Schlieren imaging has proven to be a valuable tool for detecting refractive index variations in the process zone, providing insights into gas flow behaviour, shielding gas efficiency and process plume dynamics. However, schlieren imaging typically requires specialized optical setups, making integration into industrial systems challenging. This study explores an artificial intelligence-driven cross-modality transfer approach that enables the prediction of schlieren-induced refractive index variations from coaxial imaging data, eliminating the need for a dedicated schlieren setup. A background-oriented schlieren system was used to capture reference data, while a coaxial camera recorded the melt pool and surrounding process zone during DED-LB/M. A machine learning model was trained on the combined dataset, establishing correlations between schlieren activity and intensity variations in the coaxial images. The model successfully predicted schlieren-induced disturbances, allowing for the indirect detection of gas flow instabilities and shielding gas deficiencies. The results demonstrate that artificial intelligence-based analysis of coaxial imaging can provide schlieren-equivalent process information, making it possible to monitor refractive index variations, detect process deviations and improve defect prediction in real time. This approach enhances process monitoring capabilities in DED-LB/M, enabling cost-effective, scalable and easily integrable monitoring solutions for industrial applications.