Recently, regression artificial neural networks are used to model various systems that have high dimensionality with nonlinear relations. The system under study must have enough dataset available to train the neural network. The aim of this work is to apply and experiment various options effects on feed-foreword artificial neural network (ANN) which used to obtain regression model that predicts electrical output power (EP) of combined cycle power plant based on 4 inputs. Dataset is obtained from an open online source. The work shows and explains the stochastic behavior of the regression neural, experiments the effect of number of neurons of the hidden layers. It shows also higher performance for larger training dataset size; at the other hand, it shows different effect of larger number of variables as input. In addition, two different training functions are applied and compared. Lastly, simple statistical study on the error between real values and estimated values using ANN is conducted, which shows the reliability of the model. This paper provides a quick reference to the effects of main parameters of regression neural networks.
Selective Laser Sintering (SLS) technology can be utilized to recycle residues from forestry and agriculture, thereby alleviating shortages of materials and reducing energy consumption by producing wood-plastic pieces for industrial application. The mechanical strength of wood-plastic SLS parts is low, which restricts the application of this technology. In this study, a novel type of sisal fiber/poly-(ether sulfone) (PES) composite was prepared using a polymer mixing method in order to improve the mechanical properties of SLS parts. Single-layer sintering method was adopted to determine the proper processing parameters. The mechanical properties of the parts with different ingredient ratios and different particle sizes of sisal fiber before and after post-processing were tested using a universal testing machine. The morphology was examined using scanning electron microscopy (SEM). Results showed that the mechanical properties of the printed parts were relatively enhanced; when the mixing ratio of composite powder was 10/90 wt/wt. In addition, the part fabricated by powder of particles size less than 0.105 mm (0.125 mm ≥ PS < 0.105mm) had the best mechanical strength. Moreover, the post-wax treatment significantly improved the strength of the parts, and the surfaces became smoother.
This article aims to improve the sintering quality of the sisal fiber/poly-(ether sulfone) (PES) composite (SFPC) part fabricated via selective laser sintering (SLS). The sisal fiber and PES powders were proposed as the feedstock of the SFPC powder bed for SLS. An orthogonal experimental methodology with four levels and five factors was applied to optimize the process parameters for the single-layer sintering experiment. The mechanical properties and accurate dimensions of the sintered part were tested using a universal testing machine and Vernier caliper. The preheating temperature, scanning speed, and laser power were selected as influencing factors on the mechanical properties and accuracy dimensions of the SFPC part. Furthermore, the influence factors on the quality of the sintered part were studied and analyzed. Additionally, the synthesis weighted scoring method was used to determine the optimum parameters of the SLS part. The results showed that the optimal parameters (factors) were preheating temperature of 82°C, scanning speed of 2 m s−1, laser power of 14 W, and laser wavelength of 10.6 μm. Thus, the quality of SFPC part was significantly enhanced when the optimum parameters were applied in SLS process. This article provided the main reference value for the choice of the process parameters of the biomass composite.
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