@article{EichlerBalcBremenetal.2024,
  author    = {Eichler, Fabian and Balc, Nicolae and Bremen, Sebastian and Nink, Philipp},
  title     = {Investigation of laser powder bed fusion parameters with respect to their influence on the thermal conductivity of 316L samples},
  series = {Journal of Manufacturing and Materials Processing},
  volume    = {8},
  journal   = {Journal of Manufacturing and Materials Processing},
  number    = {4},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2504-4494},
  doi       = {10.3390/jmmp8040166},
  pages     = {12 Seiten},
  year      = {2024},
  abstract  = {The thermal conductivity of components manufactured using Laser Powder Bed Fusion (LPBF), also called Selective Laser Melting (SLM), plays an important role in their processing. Not only does a reduced thermal conductivity cause residual stresses during the process, but it also makes subsequent processes such as the welding of LPBF components more difficult. This article uses 316L stainless steel samples to investigate whether and to what extent the thermal conductivity of specimens can be influenced by different LPBF parameters. To this end, samples are set up using different parameters, orientations, and powder conditions and measured by a heat flow meter using stationary analysis. The heat flow meter set-up used in this study achieves good reproducibility and high measurement accuracy, so that comparative measurements between the various LPBF influencing factors to be tested are possible. In summary, the series of measurements show that the residual porosity of the components has the greatest influence on conductivity. The degradation of the powder due to increased recycling also appears to be detectable. The build-up direction shows no detectable effect in the measurement series.},
  language  = {en}
}
@article{KohlKraemerFohryetal.2024,
  author    = {Kohl, Philipp and Kr{\"a}mer, Yoka and Fohry, Claudia and Kraft, Bodo},
  title     = {Scoping review of active learning strategies and their evaluation environments for entity recognition tasks},
  series = {Deep learning theory and applications},
  journal   = {Deep learning theory and applications},
  editor    = {Fred, Ana and Hadjali, Allel and Gusikhin, Oleg and Sansone, Carlo},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-031-66694-0 (online ISBN)},
  doi       = {10.1007/978-3-031-66694-0_6},
  pages     = {84 -- 106},
  year      = {2024},
  abstract  = {We conducted a scoping review for active learning in the domain of natural language processing (NLP), which we summarize in accordance with the PRISMA-ScR guidelines as follows: Objective: Identify active learning strategies that were proposed for entity recognition and their evaluation environments (datasets, metrics, hardware, execution time). Design: We used Scopus and ACM as our search engines. We compared the results with two literature surveys to assess the search quality. We included peer-reviewed English publications introducing or comparing active learning strategies for entity recognition. Results: We analyzed 62 relevant papers and identified 106 active learning strategies. We grouped them into three categories: exploitation-based (60x), exploration-based (14x), and hybrid strategies (32x). We found that all studies used the F1-score as an evaluation metric. Information about hardware (6x) and execution time (13x) was only occasionally included. The 62 papers used 57 different datasets to evaluate their respective strategies. Most datasets contained newspaper articles or biomedical/medical data. Our analysis revealed that 26 out of 57 datasets are publicly accessible. Conclusion: Numerous active learning strategies have been identified, along with significant open questions that still need to be addressed. Researchers and practitioners face difficulties when making data-driven decisions about which active learning strategy to adopt. Conducting comprehensive empirical comparisons using the evaluation environment proposed in this study could help establish best practices in the domain.},
  language  = {en}
}