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Wyszukujesz frazę "neural modelling" wg kryterium: Temat

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    Wyświetlanie 1-8 z 8
    Tytuł:
    Control of complex dynamic nonlinear loading process for electromagnetic mill
    Autorzy:
    Ogonowski, Szymon
    Bismor, Dariusz
    Ogonowski, Zbigniew
    Powiązania:
    https://bibliotekanauki.pl/articles/229680.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    predictive control
    pole placement
    nonlinear dynamics
    neural modelling
    electromagnetic mill
    Opis:
    Electromagnetic mill installation for dry grinding represents a complex dynamical system that requires specially designed control system. The paper presents model-based predictive control which locates closed loop poles in arbitrary places. The controller performs as gains cheduling prototype where nonlinear model – artificial recurrent neural network, is parameterized with additional measurements and serves as a basis for local linear approximation. Application of such a concept to control electromagnetic mill load allows for stable performance of the installation and assures fulfilment of the product quality as well as the optimization of the energy consumption.
    Źródło:
    Archives of Control Sciences; 2020, 30, 3; 471-500
    1230-2384
    Pojawia się w:
    Archives of Control Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Neural network approach to compressor modelling with surge margin consideration
    Autorzy:
    Loryś, Sergiusz Michał
    Orkisz, Marek
    Powiązania:
    https://bibliotekanauki.pl/articles/2091364.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    modelling
    compressor map
    neural-network
    Opis:
    Artificial neural networks are gaining popularity thank to their fast and accurate response paired with low computing power requirements. They have been proven as a method for compressor performance prediction with satisfactory results. In this paper a new approach of artificial neural networks modelling is evaluated. The auxiliary parameter of ‘relative stability margin Z’ was introduced and used in learning process. This approach connects two methods of compressor modelling such as neural networks and auxiliary parameter utilization. Two models were created, one with utilization of the ‘relative stability margin Z’ as a direct indication of surge margin of any estimated condition, and other with standard compressor parameters. The results were compared by determination of fitting, interpolation and extrapolation capabilities of both approaches. The artificial neural networks used during the process was a two-layer feed-forward neural-network with Levenberg–Marquardt algorithm with Bayesian regularization. The experimental data was interpolated to increase the amount of learning data for the neural network. With the two models created, capabilities of this relatively simple type of neural-network to approximate compressor map was also assessed.
    Źródło:
    Archives of Thermodynamics; 2022, 43, 1; 89--108
    1231-0956
    2083-6023
    Pojawia się w:
    Archives of Thermodynamics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Optimization of a thin-walled element geometry using a system integrating neural networks and finite element method
    Autorzy:
    Golewski, P.
    Gajewski, J.
    Sadowski, T.
    Powiązania:
    https://bibliotekanauki.pl/articles/351314.pdf
    Data publikacji:
    2017
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    artificial neural networks
    numerical modelling
    thin-walled element
    Opis:
    Artificial neural networks [ANNs] are an effective method for predicting and classifying variables. This article presents the application of an integrated system based on artificial neural networks and calculations by the finite element method [FEM] for the optimization of geometry of a thin-walled element of an air structure. To ensure optimal structure, the structure’s geometry was modified by creating side holes and ribs, also with holes. The main criterion of optimization was to reduce the structure’s weight at the lowest possible deformation of the tested object. The numerical tests concerned a fragment of an elevator used in the “Bryza” aircraft. The tests were conducted for networks with radial basis functions [RBF] and multilayer perceptrons [MLP]. The calculations described in the paper are an attempt at testing the FEM - ANN system with respect to design optimization.
    Źródło:
    Archives of Metallurgy and Materials; 2017, 62, 1; 435-442
    1733-3490
    Pojawia się w:
    Archives of Metallurgy and Materials
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A New Neural Networks Model for Calculating the Continuous Cooling Transformation Diagrams
    Autorzy:
    Trzaska, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/351678.pdf
    Data publikacji:
    2018
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    CCT diagram
    modelling
    neural network
    heat treatment
    steel
    Opis:
    The article shows a new model of Continuous Cooling Transformation (CCT) diagrams of structural steels and engineering steels. The modelling used artificial neural networks and a set of experimental data prepared based on 550 CCT diagrams published in the literature. The model of CCT diagrams forms 17 artificial neural networks which solve classification and regression tasks. Neural model is implemented in a computer software that enables calculation of a CCT diagram based on chemical composition of steel and its austenitizing temperature.
    Źródło:
    Archives of Metallurgy and Materials; 2018, 63, 4; 2009-2015
    1733-3490
    Pojawia się w:
    Archives of Metallurgy and Materials
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Experimental investigation and modelling of Friction Stir Processing of cast aluminium alloy AlSi9Mg
    Autorzy:
    Węglowski, M. S.
    Dymek, S.
    Hamilton, C. B.
    Powiązania:
    https://bibliotekanauki.pl/articles/200339.pdf
    Data publikacji:
    2013
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    friction stir processing
    aluminum alloys
    numerical modelling
    neural networks
    microstructure
    Opis:
    Friction Stir Processing (FSP) is a novel solid state processing technique which can be used for microstructural modification of surface layers in metallic materials. This paper analyzes the effects of FSP process parameters on spindle torque acting on the tool and on the tool temperature. It has been shown that an increase in the rotational speed brings about a decrease in the torque and an increase of temperature. For temperature estimation in the stir zone a numerical model was applied, while for predicting a relationship between the spindle torque acting on the tool, rotational and travelling speeds and the down force, the artificial neural networks approach was employed. Light and electron (scanning and transmission) microscopy investigation showed that the FSP process reduces porosity and produces a more uniform distribution of second-phase particles.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2013, 61, 4; 893-904
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Comprehensive analysis of reclamation of spent lubricating oil using green solvent: RSM and ANN approach
    Autorzy:
    Sarkar, Sayantan
    Datta, Deepshikha
    Chowdhury, Somnath
    Das, Bimal
    Powiązania:
    https://bibliotekanauki.pl/articles/2173421.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    modelling
    optimization
    extraction-flocculation
    artificial neural network
    genetic algorithm
    modelowanie
    optymalizacja
    sztuczna sieć neuronowa
    algorytm genetyczny
    Opis:
    Waste lubricating oil (WLO) is the most significant liquid hazardous waste, and indiscriminate disposal of waste lubricating oil creates a high risk to the environment and ecology. Present investigation emphasizes the re-refining of used automobile engine oil using the extraction-flocculation approach to reduce environmental hazards and convert the waste to energy. The extraction-flocculation process was modeled and optimized using response surface methodology (RSM), artificial neural network (ANN), and genetic algorithm (GA). The present study assessed parametric effects of refining time, refining temperature, solvent to waste oil ratio, and flocculant dosage. Experimental findings showed that the percentage of yield of recovered oil is to the tune of 86.13%. With the Central Composite Design approach, the maximum percentage of extracted oil is 85.95%, evaluated with 80 minutes of refining time, 50.17 C refining temperature, 7:1 solvent to waste oil ratio and flocculant dosage of 3 g/kg of solvent and 86.71% with 79.97 minutes refining time, 55.53 C refining temperature, 4.89:1 g/g solvent to waste oil ratio, 2.99 g/kg of flocculant concentration with Artificial Neural Network. A comparison shows that the ANN gives better results than the CCD approach. Physico-chemical properties of the recovered lube oil are comparable with the properties of fresh lubricating oil.
    Źródło:
    Chemical and Process Engineering; 2022, 43, 2; 119--135
    0208-6425
    2300-1925
    Pojawia się w:
    Chemical and Process Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Modelling and Analysis of the Synergistic Alloying Elements Effect on Hardenability of Steel
    Autorzy:
    Sitek, Wojciech
    Trzaska, Jacek
    Gemechu, W. F.
    Powiązania:
    https://bibliotekanauki.pl/articles/2203932.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    hardenability
    artificial neural networks
    multiple regression
    steel alloy
    modelling and simulation
    hartowność
    sztuczne sieci neuronowe
    regresja wielokrotna
    stal
    modelowanie i symulacja
    Opis:
    The paper presents a methodology of modeling relationships between chemical composition and hardenability of structural alloy steels using computational intelligence methods, that are artificial neural network and multiple regression models. Particularly, the researchers used unidirectional multilayer teaching method based on the error backpropagation algorithm and a quasi-newton methods. Based on previously known methodologies, it was found that there is no universal method of modeling hardenability, and it was also noted that there are errors related to the calculation of the curve. The study was performed on large set of experimental data containing required information on about the chemical compositions and corresponding Jominy hardenability curves for over 400 data steel heats with variety of chemical compositions. It is demonstrated that the full practical usefulness of the developed models in the selection of materials for particular applications with intended performance in the area of application.
    Źródło:
    Archives of Foundry Engineering; 2022, 22, 4; 102--108
    1897-3310
    2299-2944
    Pojawia się w:
    Archives of Foundry Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Artificial Neural Network to the Control of the Parameters of the Heat Treatment Process of Casting
    Autorzy:
    Wróbel, J.
    Kulawik, A.
    Bokota, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/382692.pdf
    Data publikacji:
    2015
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    heat treatment
    moving heat source
    artificial neural network
    numerical modelling
    control system
    heating process
    obróbka cieplna
    źródło ciepła ruchome
    sztuczna sieć neuronowa
    modelowanie numeryczne
    system sterowania
    proces nagrzewania
    Opis:
    In the paper the use of the artificial neural network to the control of the work of heat treating equipment for the long axisymmetric steel elements with variable diameters is presented. It is assumed that the velocity of the heat source is modified in the process and is in real time updated according to the current diameter. The measurement of the diameter is performed at a constant distance from the heat source (Δz = 0). The main task of the model is control the assumed values of temperature at constant parameters of the heat source such as radius and power. Therefore the parameter of the process controlled by the artificial neural network is the velocity of the heat source. The input data of the network are the values of temperature and the radius of the heated element. The learning, testing and validation sets were determined by using the equation of steady heat transfer process with a convective term. To verify the possibilities of the presented algorithm, based on the solve of the unsteady heat conduction with finite element method, a numerical simulation is performed. The calculations confirm the effectiveness of use of the presented solution, in order to obtain for example the constant depth of the heat affected zone for the geometrically variable hardened axisymmetric objects.
    Źródło:
    Archives of Foundry Engineering; 2015, 15, 1; 119-124
    1897-3310
    2299-2944
    Pojawia się w:
    Archives of Foundry Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-8 z 8

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