@article{43046,
  abstract     = {{In the laser sintering technology, the semi-crystalline polymer material is exposed to elevated temperatures during processing, which leads to serious material ageing for most materials. This has already been investigated intensively by various authors. However, the ageing of the material at ambient temperatures during shelf life has not been the focus so far. The need to analyse the shelf life can be derived from an ecological and economic point of view. This work is focusing on the shelf life of PA2200 (PA12). To reduce the potential influences of powder production fluctuations, two different powder batches stored for 5.5 years and 6.5 years are investigated and compared to a reference powder produced 0.5 years before these investigations. Multiple powder analyses and part characterisations have been performed. A significant yellowing and molecular chain length reduction can be derived from the measurement results. Whereas the influence on mechanical part performance was minor, the parts built with the stored powders are more yellowish. As it is most likely that this is due to the consumption of polyamide stabilisers, it can be assumed that these parts will be subject to significantly faster ageing. Therefore, it is still not recommended to use the stored powders for critical parts or light intense and humid environments.}},
  author       = {{Klippstein, Sven Helge and Kletetzka, Ivo and Sural, Ilknur and Schmid, Hans-Joachim}},
  journal      = {{The International Journal of Advanced Manufacturing Technology }},
  keywords     = {{Selective laser sintering, Shelf life, Polyamide 12, powder, PA2200, material ageing}},
  publisher    = {{Springer}},
  title        = {{{Influence of a prolonged shelf time on PA12 laser sintering powder and resulting part properties}}},
  doi          = {{https://doi.org/10.1007/s00170-023-11243-1}},
  year         = {{2023}},
}

@inproceedings{9879,
  abstract     = {{Application of prognostics and health management (PHM) in the field of Proton Exchange Membrane (PEM) fuel cells is emerging as an important tool in increasing the reliability and availability of these systems. Though a lot of work is currently being conducted to develop PHM systems for fuel cells, various challenges have been encountered including the self-healing effect after characterization as well as accelerated degradation due to dynamic loading, all which make RUL predictions a difficult task. In this study, a prognostic approach based on adaptive particle filter algorithm is proposed. The novelty of the proposed method lies in the introduction of a self-healing factor after each characterization and the adaption of the degradation model parameters to fit to the changing degradation trend. An ensemble of five different state models based on weighted mean is then developed. The results show that the method is effective in estimating the remaining useful life of PEM fuel cells, with majority of the predictions falling within 5\% error. The method was employed in the IEEE 2014 PHM Data Challenge and led to our team emerging the winner of the RUL category of the challenge.}},
  author       = {{Kimotho, James Kuria  and Meyer, Tobias and Sextro, Walter}},
  booktitle    = {{Prognostics and Health Management (PHM), 2014 IEEE Conference on}},
  keywords     = {{ageing, particle filtering (numerical methods), proton exchange membrane fuel cells, remaining life assessment, PEM fuel cell prognostics, PHM, RUL predictions, accelerated degradation, adaptive particle filter algorithm, dynamic loading, model parameter adaptation, prognostics and health management, proton exchange membrane fuel cells, remaining useful life estimation, self-healing effect, Adaptation models, Data models, Degradation, Estimation, Fuel cells, Mathematical model, Prognostics and health management}},
  pages        = {{1--6}},
  title        = {{{PEM fuel cell prognostics using particle filter with model parameter adaptation}}},
  doi          = {{10.1109/ICPHM.2014.7036406}},
  year         = {{2014}},
}