@inproceedings{Boehm2022, author = {B{\"o}hm, Stefan-Andreas}, title = {Adaptable Machining Process Identification based on Expert Knowledge and Artificial Intelligence}, series = {Organic Computing Doctoral Dissertation Colloquium 2021}, booktitle = {Organic Computing Doctoral Dissertation Colloquium 2021}, editor = {Tomforde, Sven and Krupitzer, Christian}, publisher = {kassel university press}, address = {Kassel}, doi = {10.17170/kobra-202202215780}, pages = {1 -- 14}, year = {2022}, abstract = {The demand for product individuality increased enormously in recent years and thus affects directly manufacturers and their employees. Due to the increasing demand for batch-size-one production, every product needs specific manufacturing processes. Usually, employees determine these manufacturing steps with provided product data. This research proposal aims to contribute to the extraction of machining processes from product data and their assignment to suitable machinery. We plan to develop an organic computing system based on artificial intelligence methods to solve these problems by including customer-specific designs and employee expertise. The overall objective is to support employees in manufacturing facilities by simplifying the manufacturer and customer interaction.}, language = {en} } @unpublished{BoehmNeumayerKrameretal.2021, author = {B{\"o}hm, Stefan-Andreas and Neumayer, Martin and Kramer, Oliver and Schiendorfer, Alexander and Knoll, Alois}, title = {Comparing Heuristics, Constraint Optimization, and Reinforcement Learning for an Industrial 2D Packing Problem}, doi = {10.48550/arXiv.2110.14535}, year = {2021}, abstract = {Cutting and Packing problems are occurring in different industries with a direct impact on the revenue of businesses. Generally, the goal in Cutting and Packing is to assign a set of smaller objects to a set of larger objects. To solve Cutting and Packing problems, practitioners can resort to heuristic and exact methodologies. Lately, machine learning is increasingly used for solving such problems. This paper considers a 2D packing problem from the furniture industry, where a set of wooden workpieces must be assigned to different modules of a trolley in the most space-saving way. We present an experimental setup to compare heuristics, constraint optimization, and deep reinforcement learning for the given problem. The used methodologies and their results get collated in terms of their solution quality and runtime. In the given use case a greedy heuristic produces optimal results and outperforms the other approaches in terms of runtime. Constraint optimization also produces optimal results but requires more time to perform. The deep reinforcement learning approach did not always produce optimal or even feasible solutions. While we assume this could be remedied with more training, considering the good results with the heuristic, deep reinforcement learning seems to be a bad fit for the given use case.}, language = {en} } @unpublished{BoehmZagarRissetal.2024, author = {B{\"o}hm, Stefan-Andreas and Zagar, Bare Luka and Riß, Fabian and Kort{\"u}m, Christian and Knoll, Alois Christian}, title = {Automated Feature Recognition in Surface Cad Models Based on Graph Neural Networks}, doi = {10.2139/ssrn.4772779}, year = {2024}, abstract = {Driven by increasing customer demands, manufacturing processes encompass increasingly intricate workflows. The industry relies on computer-aided process planning software to effectively manage the intricacies of these complex manufacturing processes. This software is crucial to analyze computer-aided design data for a product and determining the required machining steps. However, a notable challenge arises, particularly in the case of custom products, where the machining steps can significantly vary depending on the available machinery and the employees' preferences.This study introduces a configurable automated feature recognition framework based on expert knowledge. Experts can encode their insights and expertise within this framework using a configurable synthetic data generator. Modern graph neural networks learn from the data generated by this data generator, achieving an average recognition accuracy up to 80\% (F1-score) with a runtime performance of 21.98 milliseconds per model. Importantly, it accomplishes this even when confronted with highly intersecting machining features without requiring data conversion into alternative formats, such as voxel or pixel representations.}, language = {en} } @inproceedings{Seliger2024, author = {Seliger, Norbert}, title = {Reconfigurable Polygonal Planar Inductor with Bistable Snapping Mechanism}, series = {2024 IEEE Open Conference of Electrical, Electronic and Information Sciences (eStream), Vilnius, Lithuania}, booktitle = {2024 IEEE Open Conference of Electrical, Electronic and Information Sciences (eStream), Vilnius, Lithuania}, isbn = {979-8-3503-5241-2}, issn = {2690-8506}, doi = {10.1109/eStream61684.2024.10542595}, pages = {1 -- 6}, year = {2024}, abstract = {A mechanically tunable planar single-turn inductor in air is proposed. The bistable flexible deflection of axially compressed wire segments is used for variation of the loop area of regular polygonal loop inductors. We present analytical expressions for the loop inductance and confirm the theoretical findings by magneto-quasi-static field simulation on exemplary polygonal circuits.}, language = {en} } @misc{Berger2024, author = {Berger, Yvonne}, title = {Doing transnational biographies in the PRC - Educational upward mobility, social stratification and family}, editor = {Berger, Yvonne}, year = {2024}, abstract = {In the 1990s, China implemented educational reforms to expand access to education. However, the focus on neoliberal ideologies and educational success has led to significant social implications in the context of social strata. As a result, students now face pressure to perform in the educational system to achieve success and therefore social upward mobility. Families encourage their children to pursue education as a means of social advancement, and this has become a defining feature of Chinese society to have or not have social quality (suzhi). In this presentation, I will discuss how this focus on education impacts the social upward mobility and biographical orientations of elite students in China. I will discuss social mobility and stratification through the lens of habitus theory. According to P. Bourdieu, this theoretical perspective suggests that achievement in the context of social mobility is not solely dependent on economic capital but also on cultural capital. Biographically, this is highly interesting, as families represent the essential point of reference for educational decisions and impact making. Families play a crucial role in shaping educational decisions and aspirations; if individuals lack the necessary economic, social and cultural capital, they may seek opportunities in transnational contexts as a form of outsourcing and even biographical illusion.}, language = {en} } @inproceedings{HerzLorenzMuscatetal.2024, author = {Herz, Jonas and Lorenz, Katharina and Muscat, Dirk and Str{\"u}bbe, Nicole}, title = {Polymeric core-shell and mono-material fibers for concrete reinforcement}, series = {AIP Conference Proceedings}, volume = {3158}, booktitle = {AIP Conference Proceedings}, number = {1}, organization = {38th International Conference of the Polymer Processing Society (PPS-38)}, doi = {10.1063/5.0204946}, year = {2024}, abstract = {A reinforcement of concrete structures is needed because of the low tensile strength of concrete. Corrosion of the usually used steel reinforcments cause issues during time. Therfore, alternative reinforcements produced from non-corosive materials - like polymer fibers - become more interesting. A polymer fiber has to reach high mechanical properties and a good bonding ability to concrete. Both properties can be influenced by the selection of polymer and the used production process. Two polymers, polypropylene and aliphatic polyketone are tested within this study. First mono-material fibers of each material are produced and tested, later the materials are combined in a core-shell fiber. All fibers are produced by standard extrusion or coextrusion and a later drawing process. The mechanical properties are determined by tensile tests. The calculation of interfacial shear strength from single fiber pull-out tests allows an evaluation of the bonding ability. Additionally, fiber surface before and after pull-out is examined using reflected light microscopy. Contact angle measurements are done to evaluate possible influences of the surface energy and polarity. Density measurements are used to compare weight potential of the different mono-material and core-shell fibers. The results show good mechanical properties for all fiber materials. The interfacial shear strength is ≈ 2-3 times higher for fibers with polyketone compared to the ones with polypropylene at the fibers surface, which can be explained by higher surface energy and polarity of the polyketone compared to polypropylene and different surface deformation during pull-out. Lower densities are reached by fibers containing polypropylene.}, language = {en} } @article{KraftQuaysonKellner2024, author = {Kraft, Sarah Katharina and Quayson, Matthew and Kellner, Florian}, title = {Assessing the German Act on Corporate Due Diligence Obligations in Supply Chains: a perspective from the smallholder cocoa farmer}, series = {Frontiers in Sustainability}, volume = {5}, journal = {Frontiers in Sustainability}, doi = {10.3389/frsus.2024.1376619}, year = {2024}, abstract = {This research examines the potential outputs, outcomes, and impacts of the German Act on Corporate Due Diligence Obligations in Supply Chains (LkSG) on the smallholder cocoa farmers in West Africa. The study primarily relies on a literature review and an impact pathway to conduct a systematic analysis to identify the potential effects of the LkSG on smallholder cocoa farmers. The findings indicate that some, but not all of the risks addressed by the LkSG align with those faced by smallholder cocoa farmers and their families. Additionally, the research also reveals weaknesses, particularly in managing environmental risks, which the LkSG does not adequately cover. Our findings show that in the short- and medium-term, the LkSG has no potential effects on smallholder cocoa farmers. Furthermore, the potential positive impacts of the law on smallholder cocoa farmers will take a long time to realize, as the LkSG considers primarily tier-1 suppliers. Companies in Germany might reassess their supply chains to strive for an LkSG-risk-free supply chain, which could in the long term have sustained impacts on smallholder cocoa farmers. However, we recommend a comprehensive risk analysis of the cocoa supply chain to enhance the human rights of cocoa farmers.}, language = {en} } @article{WohlschlaegerVersenLoederetal.2024, author = {Wohlschl{\"a}ger, Maximilian and Versen, Martin and L{\"o}der, Martin G. J. and Laforsch, Christian}, title = {Identification of different plastic types and natural materials from terrestrial environments using fluorescence lifetime imaging microscopy.}, series = {Analytical and Bioanalytical Chemistry}, journal = {Analytical and Bioanalytical Chemistry}, doi = {10.1007/s00216-024-05305-w}, year = {2024}, abstract = {Environmental pollution by plastics is a global issue of increasing concern. However, microplastic analysis in complex environmental matrices, such as soil samples, remains an analytical challenge. Destructive mass-based methods for microplastic analysis do not determine plastics' shape and size, which are essential parameters for reliable ecological risk assessment. By contrast, nondestructive particle-based methods produce such data but require elaborate, time-consuming sample preparation. Thus, time-efficient and reliable methods for microplastic analysis are needed. The present study explored the potential of frequency-domain fluorescence lifetime imaging microscopy (FD-FLIM) for rapidly and reliably identifying as well as differentiating plastics and natural materials from terrestrial environments. We investigated the fluorescence spectra of ten natural materials from terrestrial environments, tire wear particles, and eleven different transparent plastic granulates <5 mm to determine the optimal excitation wavelength for identification and differentiation via FD-FLIM under laboratory conditions. Our comparison of different excitation wavelengths showed that 445 nm excitation exhibited the highest fluorescence intensities. 445 nm excitation was also superior for identifying plastic types and distinguishing them from natural materials from terrestrial environments with a high probability using FD-FLIM. We could demonstrate that FD-FLIM analysis has the potential to contribute to a streamlined and time-efficient direct analysis of microplastic contamination. However, further investigations on size-, shape-, color-, and material-type detection limitations are necessary to evaluate if the direct identification of terrestrial environmental samples of relatively low complexity, such as a surface inspection soil, is possible.}, language = {en} } @article{WohlschlaegerVersenLoederetal.2024, author = {Wohlschl{\"a}ger, Maximilian and Versen, Martin and L{\"o}der, Martin G. J. and Laforsch, Christian}, title = {A promising method for fast identification of microplastic particles in environmental samples: A pilot study using fluorescence lifetime imaging microscopy}, series = {Heliyon}, volume = {10}, journal = {Heliyon}, number = {3}, doi = {10.1016/j.heliyon.2024.e25133}, year = {2024}, abstract = {Microplastic pollution of the environment has been extensively studied, with recent studies focusing on the prevalence of microplastics in the environment and their effects on various organisms. Identification methods that simplify the extraction and analysis process to the point where the extraction can be omitted are being investigated, thus enabling the direct identification of microplastic particles. Currently, microplastic samples from environmental matrices can only be identified using time-consuming extraction, sample processing, and analytical methods. Various spectroscopic methods are currently employed, such as micro Fourier-transform infrared, attenuated total reflectance, and micro Raman spectroscopy. However, microplastics in environmental matrices cannot be directly identified using these spectroscopic methods. Investigations using frequency-domain fluorescence lifetime imaging microscopy (FD-FLIM) to identify and differentiate plastics from environmental materials have yielded promising results for directly identifying microplastics in an environmental matrix. Herein, two artificially prepared environmental matrices that included natural soil, grass, wood, and high-density polyethylene were investigated using FD-FLIM. Our first results showed that we successfully identified one plastic type in the two artificially prepared matrices using FD-FLIM. However, further research must be conducted to improve the FD-FLIM method and explore its limitations for directly identifying microplastics in environmental samples.}, language = {en} } @inproceedings{WohlschlaegerLeiterDietlmeieretal.2023, author = {Wohlschl{\"a}ger, Maximilian and Leiter, Nina and Dietlmeier, Maximilian and L{\"o}der, Martin G.J. and Versen, Martin and Laforsch, Christian}, title = {Comparison of Two Classification Methods Trained with FD-FLIM Data to Identify and Distinguish Plastics from Environmental Materials}, series = {2023 International Joint Conference on Neural Networks (IJCNN)}, booktitle = {2023 International Joint Conference on Neural Networks (IJCNN)}, doi = {10.1109/IJCNN54540.2023.10191054}, pages = {1 -- 9}, year = {2023}, abstract = {Previous research on identifying plastic types and differentiating plastics from environmental material is promising by utilizing the specific fluorescence lifetime, but the evaluation still has to be automated. Therefore, an automated Gaussian analysis is developed for evaluating frequency-domain fluorescence lifetime images of plastics and environmental materials. Furthermore, we applied a "Multilayer Perceptron" and "Random Forest Classifier" to the data resulting from the Gaussian analysis of the frequency domain fluorescence lifetime imaging microscopy data. The classification results show high F1-scores, whereby the best "Multilayer Perceptron" and "Random Forest Classifier" achieved an F1-score of 90\%. Thus, identifying and differentiating plastics and environmental materials is possible by applying a "Multilayer Perceptron" or "Random Forest Classifier" to the Gaussian-analyzed imaged fluorescence lifetime data.}, language = {en} }