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.

Chlorinated trityl radicals functionalized with electron-donating groups are promising red-emitting materials for optoelectronic and spintronic applications, overcoming the spin-statistical limit of conventional emitters. Donor functionalization induces charge transfer character, enhancing photoluminescence quantum yield, which depends on the donor strength and its orientation. However, donor-functionalized tris(trichlorophenyl)methyl radicals often show lower quantum yield than their perchlorinated derivatives, likely due to weaker donor-acceptor electronic coupling and enhanced non-radiative decay. A novel trityl derivative is presented with two additional chlorines that restrict the orientation of the donor to a nearly perpendicular arrangement toward the trityl plane, minimizing vibronic coupling and non-radiative losses. Spectroscopic and computational studies reveal that this steric constraint improves the photoluminescence quantum yield compared to the tris(trichlorophenyl)methyl analogs. These findings highlight the potential of donor-acceptor decoupling to enable efficient, redshifted emission, offering a design strategy for high-performance radical emitters.

Blue-green infrastructure (BGI) options are considered to be more sustainable practices for water management and bring arange of benefits over and above water management. Davidshall in Malmö, Sweden, has been used as a case area toassess the multiple benefits of implementing BGI, considering seven alternative BGI schemes systematically developed alongtwo scales: naturalness (i.e. more/less engineered/complex) and spatial distribution (e.g. decentral vs. end-of-pipe). The baseline alternative was the existing situation. The Benefit Estimation Tool (B£ST) was used to carry out a socio-economicassessment. The overall benefits varied significantly (two orders of magnitude), depending on the BGI scheme implemented:the greatest values were associated with natural decentral, natural decentral/end-of-pipe, and engineered decentral/endof-pipe alternatives, those including sub-surface and open dry detention, stormwater tree pits, and rain gardens. The threeB£ST categories providing the greatest benefits were enhancing amenity, benefiting health, and reducing flooding. Cultural ecosystem services were provided by all alternatives, and two alternatives (natural decentral and natural decentral/end-of-pipe)also provided regulating ecosystem services. The study showed that amenity and health were the most significant benefitsof BGI implementation, contrasting with the main aim of BGI implementation, which was stormwater management (water quality and flood protection).

Effective information sharing is essential for improving the efficiency and effectiveness of supply chains. However, the specific impact of various information sharing factors on mitigating the bullwhip effect has not been extensively investigated in existing literature. This study addresses this gap by systematically reviewing and analyzing research from 2015 to 2024, focusing on how different information sharing factors contribute to bullwhip effect mitigation. We reviewed 57 articles to gain insights into five critical factors: what information is shared, how it is shared, with whom it is shared, why it is shared, and the direction of information sharing. Our analysis indicates that “why to share” and “what to share” are the most frequently studied dimensions, while “how to share,” “with whom to share,” and “the direction of information sharing” have been less explored. This study underscores the need for further research on these less examined factors and enhances understanding of their role in mitigating the bullwhip effect. Additionally, it provides a future research agenda to direct further investigations in this domain.

Flotation separation based on particle surface properties presents challenges in processing ultrafine particles (−50 µm). The MAGFLO system is an innovative retrofit approach that uses an electromagnet to enhance reverse ultrafine particle flotation. The system can be implemented in most industrial steel and stainless-steel flotation cells as a retrofit approach. Particle size analyses of MAGFLO products under different conditions, based on over 250 experiments, indicated that no permanent magnetic aggregation occurred within magnetite particles during various processes. Without depressants, MAGFLO could recover over 95 % of the magnetite in the stainless-steel cell. However, while increasing the magnetic field improved the metallurgical response in the steel cell, it could reduce separation efficiency in the stainless-steel cell. The 30-second on–off electromagnet interval reduced gangue entrapment by over 10 % in the steel cell. Magnetic interactions varied by cell type: in steel cells, the magnetic field induced particle aggregation on the cell walls, thereby reducing the probability of bubble attachment, whereas in stainless-steel cells, the magnetism directly enhanced temporary particle aggregation. These findings demonstrate the promising potential of MAGFLO for various material applications.

Deep learning-based object detection algorithms enable the simultaneous classification and localization of any number of objects in image data. Many of these algorithms are capable of operating in real-time on high resolution images, attributing to their widespread usage across many fields. We present an end-to-end object detection pipeline designed for rare event searches for the Migdal effect, at real-time speeds, using high-resolution image data from the scientific CMOS camera readout of the MIGDAL experiment. The Migdal effect in nuclear scattering, critical for sub-GeV dark matter searches, has yet to be experimentally confirmed, making its detection a primary goal of the MIGDAL experiment. The Migdal effect forms a composite rare event signal topology consisting of an electronic and nuclear recoil sharing the same vertex. Crucially, both recoil species are commonly observed in isolation in the MIGDAL experiment, enabling us to train YOLOv8, a state-of-the-art object detection algorithm, on real data. Topologies indicative of the Migdal effect can then be identified in science data via pairs of neighboring or overlapping electron and nuclear recoils. Applying selections to real data that retain 99.7% signal acceptance in simulations, we demonstrate our pipeline to reduce a sample of 20 million recorded images to fewer than 1000 frames, thereby transforming a rare search into a much more manageable search. More broadly, we discuss the applicability of using object detection to enable data-driven machine learning training for other rare event search applications such as neutrinoless double beta decay searches and experiments imaging exotic nuclear decays.

The global drive toward decarbonization has spurred industry interest in the compact steel production (CSP) process for manufacturing automotive steel sheets. Understanding the hot deformation behavior, particularly the metadynamic softening mechanism occurring between passes, is essential for evaluating process feasibility under CSP process. This study investigates the metadynamic softening behavior of CP800 steel intended for CSP applications, utilizing isothermal double compression tests performed at the deformation temperatures of 1173, 1273, and 1373 K, strain rates of 0.1,1, and 5.0 s−1, and the interpass times of 1, 10, and 20 s. The softening behavior was assessed through the deformation flow stress–strain curves under varying conditions, and a kinetic equation of metadynamic recrystallization was proposed and validated against experimental data. Additionally, the effect of initial austenite grain sizes of 42 μm and 92 μm on metadynamic recrystallization were analyzed. Results indicate that the final rolling pass temperature should exceed 1173 K to prevent mixed grain structures. Although grain refinement induced by the metadynamic recrystallization in CP800 steel was found to be independent of initial grain size, the final grain size itself remained sensitive to the initial grain dimensions. Adopting lower holding temperature and shorter holding durations prior to rolling is advisable for energy-efficient CSP, provided compositional homogeneity and suitable deformation temperatures are maintained. These insights contribute valuable guidance for optimization CSP process in the production of CP800 steel.

Freshwater supply systems are considered as an important component within urban water systems. Although the development of freshwater supply systems may have significant impact on the environment, there have been only a few studies examining its environmental effects. This paper assesses the environmental impact of four pipeline materials in freshwater supply system using life cycle assessment following ISO 14040–14044 standards. The SimaPro 9.6.0.1 software was used for life cycle analysis. The results indicated that steel has a greater environmental impact in most impact categories during the pipe manufacturing phase than other pipeline materials. During the installation phase, two types of trenches were considered for plastic pipelines and steel pipelines installation and found that the plastic pipe trench experiences its greatest impact during installation phase. To showcase the practicality of the suggested approach, a segment of the Seri Iskandar freshwater supply system was chosen as a case study. The findings revealed that by substituting a portion of the pipes with environmentally sustainable materials, the environmental impact during manufacturing and materials phase of pipelines used for construction of FWSS can be reduced by 14% in fossil resource scarcity, 19% in ozone layer depletion, 20% in ionization radiation, 22% in climate change, and 25% in marine ecotoxicity potential.

Powder bed fusion laser beam (PBF-LB) is particularly effective for fabricating compositionally complex alloys such as high-entropy alloys (HEAs) or medium-entropy alloys (MEAs). Fabricating non-equiatomic metastable MEAs using PBF-LB can lead to the formation of unique microstructures that enhance the mechanical performance of these alloys. Nevertheless, plastic anisotropy in materials prepared by additive manufacturing routes including PBF-LB remains to be a technical challenge. This work presents the fabrication of a metastable non-equiatomic Co45Cr25(FeNi)30 MEA using PBF-LB. As-printed samples exhibited the formation of nano-scaled ε-martensite (HCP) phase along with the FCC phase. The HCP phase exhibited Shoji-Nishiyama orientation relationship with the FCC phase. High energy synchrotron X-ray diffraction (HEXRD) and electron backscatter diffraction (EBSD) in-situ tensile testing were employed to investigate the influence of the HCP phase on the alloy's deformation behavior. The presence of the HCP phase initiates stress-induced martensitic transformation well below the macroscopic yield strength. This transformation led to the non-linear stress and strain response for the FCC phase. Further straining resulted in significant load partitioning, with the HCP phase taking the majority of the load as it formed, significantly strain hardening the alloy and reducing the plastic anisotropy induced by texture in the as-printed material.

Sedimentary basin-hosted manganese oxides may represent an important yet underexplored source of critical metals. Here we present a stratigraphic, textural, mineralogical, and compositional characterization of Mn-oxide nodules, coatings, and veins in the Pisco onshore forearc basin, Peru. The Mn-oxide mineralization is stratabound within marine sandstone, siltstone, and tuff from the Neogene Chilcatay and Pisco formations. X-ray diffraction and electron microprobe analyses identify the Mn oxides as cryptomelane (± hollandite) and todorokite, which cement detrital grains and fossilize biological remains. Bulk chemical analyses of nodules, coatings, and veins reveal significant cobalt enrichment (mean = 0.17 ± 0.15 wt% Co; up to 0.63 wt% Co), corroborated by electron probe microanalysis of individual Mn oxide phases (mean = 0.37 ± 0.33 wt% Co; up to 2.1 wt% Co). The stratigraphic control, biomorphic replacement, mineralogy, and chemical composition collectively indicate a diagenetic origin for the Mn-oxide mineralization. The formation pathway likely involved organic matter decay or brine-hydrocarbon interactions coupled with Mn and Fe reduction, resulting in metal-enriched porewaters that circulated along structures and permeable horizons. Subsequent precipitation under oxygenated conditions occurred during late Pliocene uplift and exposure of the East Pisco Basin. This study demonstrates that diagenetic Mn oxides exposed in onshore basins represent a potential resource for manganese and critical elements such as cobalt.

Alzheimer’s Disease (AD) poses a major challenge for healthcare systems worldwide, as timely and accurate diagnosis is crucial for patient management and outcome improvement. While experienced medical professionals can often identify AD through conventional assessment methods, limited resources and growing patient populations make large-scale and rapid screening increasingly necessary. In this work, we explore whether the FixMatch algorithm—a semi-supervised learning approach—can aid in classifying Alzheimer’s Disease (AD), Mild Cognitive Impairment (MCI), and Cognitively Normal (CN) by using the ADNI fMRI dataset of 5,182 images. This approach supplements rather than replaces clinical expertise, offering a faster, more standardized classification process where expert labeling is limited. We first assessed various supervised models and determined that VGG19-FFT provided the strongest balance of classification accuracy and computational efficiency. Integrating VGG19-FFT into FixMatch as the teacher model, initial tests using 10% labeled and 90% unlabeled data yielded modest results. A more systematic examination of different labeled-to-unlabeled data splits revealed that a 60:40 ratio enabled FixMatch to achieve classification accuracies of 100% for AD, 99% for CN, and 99% for MCI—on par with fully supervised training. This outcome highlights the potential of FixMatch to significantly reduce labeling requirements, a particular advantage in resource-constrained settings where expert annotations are costly. By striking an effective balance between labeling effort and model performance, the identified 60:40 ratio helps make advanced diagnostic methods both feasible and practical in real-world clinical applications.