@article{MeiabadiMoradiKaramimoghadametal.,
  author    = {Meiabadi, Mohammad Saleh and Moradi, Mahmoud and Karamimoghadam, Mojtaba and Ardabili, Sina and Bodaghi, Mahdi and Shokri, Manouchehr and Mosavi, Amir Hosein},
  title     = {Modeling the Producibility of 3D Printing in Polylactic Acid Using Artificial Neural Networks and Fused Filament Fabrication},
  series = {polymers},
  volume    = {2021},
  journal   = {polymers},
  number    = {Volume 13, issue 19, article 3219},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/polym13193219},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20220110-45518},
  pages     = {1 -- 21},
  abstract  = {Polylactic acid (PLA) is a highly applicable material that is used in 3D printers due to some significant features such as its deformation property and affordable cost. For improvement of the end-use quality, it is of significant importance to enhance the quality of fused filament fabrication (FFF)-printed objects in PLA. The purpose of this investigation was to boost toughness and to reduce the production cost of the FFF-printed tensile test samples with the desired part thickness. To remove the need for numerous and idle printing samples, the response surface method (RSM) was used. Statistical analysis was performed to deal with this concern by considering extruder temperature (ET), infill percentage (IP), and layer thickness (LT) as controlled factors. The artificial intelligence method of artificial neural network (ANN) and ANN-genetic algorithm (ANN-GA) were further developed to estimate the toughness, part thickness, and production-cost-dependent variables. Results were evaluated by correlation coefficient and RMSE values. According to the modeling results, ANN-GA as a hybrid machine learning (ML) technique could enhance the accuracy of modeling by about 7.5, 11.5, and 4.5\% for toughness, part thickness, and production cost, respectively, in comparison with those for the single ANN method. On the other hand, the optimization results confirm that the optimized specimen is cost-effective and able to comparatively undergo deformation, which enables the usability of printed PLA objects.},
  subject      = {3D-Druck},
  language  = {en}
}
@article{LashkarAraKalantariSheikhKhozanietal.,
  author    = {Lashkar-Ara, Babak and Kalantari, Niloofar and Sheikh Khozani, Zohreh and Mosavi, Amir},
  title     = {Assessing Machine Learning versus a Mathematical Model to Estimate the Transverse Shear Stress Distribution in a Rectangular Channel},
  series = {Mathematics},
  volume    = {2021},
  journal   = {Mathematics},
  number    = {Volume 9, Issue 6, Article 596},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/math9060596},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20210504-44197},
  pages     = {15},
  abstract  = {One of the most important subjects of hydraulic engineering is the reliable estimation of the transverse distribution in the rectangular channel of bed and wall shear stresses. This study makes use of the Tsallis entropy, genetic programming (GP) and adaptive neuro-fuzzy inference system (ANFIS) methods to assess the shear stress distribution (SSD) in the rectangular channel. To evaluate the results of the Tsallis entropy, GP and ANFIS models, laboratory observations were used in which shear stress was measured using an optimized Preston tube. This is then used to measure the SSD in various aspect ratios in the rectangular channel. To investigate the shear stress percentage, 10 data series with a total of 112 different data for were used. The results of the sensitivity analysis show that the most influential parameter for the SSD in smooth rectangular channel is the dimensionless parameter B/H, Where the transverse coordinate is B, and the flow depth is H. With the parameters (b/B), (B/H) for the bed and (z/H), (B/H) for the wall as inputs, the modeling of the GP was better than the other one. Based on the analysis, it can be concluded that the use of GP and ANFIS algorithms is more effective in estimating shear stress in smooth rectangular channels than the Tsallis entropy-based equations.},
  subject      = {Maschinelles Lernen},
  language  = {en}
}