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    Wyświetlanie 1-6 z 6
    Tytuł:
    Parametric Analyses on Compressive Strength of Furan No Bake Mould System Using ANN
    Autorzy:
    Acharya, S. G.
    Vadher, J. A.
    Powiązania:
    https://bibliotekanauki.pl/articles/379929.pdf
    Data publikacji:
    2016
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    ANN method
    compressive strength
    FNB mould
    Matlab version
    R2015a version 8.3
    metoda ANN
    wytrzymałość na ściskanie
    forma FNB
    Matlab
    Opis:
    Casting is the most widely used manufacturing technique. Furan No-bake mould system is very widely accepted in competitive foundry industries due to its excellent characteristics of producing heavy and extremely difficult castings. These castings have excellent surface finish and high dimensional stability. Self setting and high dimensional stability are the key characteristics of FNB mould system which leads to reduce production cycle time for foundry industries which will ultimately save machining cost, labour cost and energy. Compressive strength is the main aspect of furan no bake mould, which can be improved by analyzing the effect of various parameters on it. ANN is a useful technique for determining the relation of various parameters like Grain Fineness Number, Loss on Ignition, pH, % resin and temperature of sand with compressive strength of the FNB mould. Matlab version: R2015a version 8.3 software with ANN tool box can be used to gain output of relation. This paper deals with the representation of relationship of various parameters affecting on the compressive strength of FNB mould.
    Źródło:
    Archives of Foundry Engineering; 2016, 16, 4; 5-10
    1897-3310
    2299-2944
    Pojawia się w:
    Archives of Foundry Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Prediction of Tunnel Cross-Sectional Area After Blastin
    Autorzy:
    Nguyen, Chi Thanh
    Nguyen, Nghia Viet
    Powiązania:
    https://bibliotekanauki.pl/articles/25212147.pdf
    Data publikacji:
    2023
    Wydawca:
    Polskie Towarzystwo Przeróbki Kopalin
    Tematy:
    ANN
    SVR
    tunnel
    drilling-blasting method
    cross-sectional area of tunnel
    prediction
    tunele
    Opis:
    In this paper, two methods to predict and calculate the area of the tunnel face after the blasting were used. The first one is an artificial intelligence method using an artificial neural network system (ANN) model, and the second one – the support vector regression (SVR). After building predictive models for the area of the tunnel face after blasting by both methods, on the basis of comparing the results obtained in both methods, the performance of these models was assessed through the root mean square error RMSE and the coefficient of determination R2. RMSE and R2 values of the artificial neural network system (ANN) model were obtained as 0.1473 and 0.903 in training datasets, respectively. These values are 0.1497 and 0.9107 in testing datasets. In the SRV model, RMSE and R2 were equaled to 0.1228 and 0.9331 in training datasets, respectively. These values are 0.1708 and 0.9055, respectively in testing datasets. It can be concluded that artificial intelligence using ANN and SVM models can be used to predict the area of the tunnel face after blasting with high accuracy.
    Źródło:
    Inżynieria Mineralna; 2023, 2; 39--47
    1640-4920
    Pojawia się w:
    Inżynieria Mineralna
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Two scales, hybrid model for soils, involving artificial neural network and finite element procedure
    Autorzy:
    Krasiński, M.
    Lefik, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/178935.pdf
    Data publikacji:
    2014
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    soil model
    neural network
    finite element method
    hybrid FE-ANN model
    model gleby
    sieci neuronowe
    metoda elementów skończonych
    model hybrydowy FE-ANN
    Opis:
    A hybrid ANN-FE solution is presented as a result of two level analysis of soils: a level of a laboratory sample and a level of engineering geotechnical problem. Engineering properties of soils (sands) are represented directly in the form of ANN (this is in contrast with our former paper where ANN approximated constitutive relationships). Initially the ANN is trained with Duncan formula (Duncan and Chang [2]), then it is re-trained (calibrated) with some available experimental data, specific for the soil considered. The obtained approximation of the constitutive parameters is used directly in finite element method at the level of a single element at the scale of the laboratory sample to check the correct representation of the laboratory test. Then, the finite element that was successfully tested at the level of laboratory sample is used at the macro level to solve engineering problems involving the soil for which it was calibrated
    Źródło:
    Studia Geotechnica et Mechanica; 2014, 36, 2; 29-36
    0137-6365
    2083-831X
    Pojawia się w:
    Studia Geotechnica et Mechanica
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Quantum-inspired method of neural modeling of the day-ahead market of the Polish electricity exchange
    Autorzy:
    Tchórzewski, Jerzy
    Ruciński, Dariusz
    Powiązania:
    https://bibliotekanauki.pl/articles/2183468.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Instytut Badań Systemowych PAN
    Tematy:
    artificial neural networks
    day-ahead market
    dequantization with ANN
    neural modeling
    quantum inspired method
    quantum computing
    Polish Electricity Exchange
    system quantization
    Opis:
    The paper presents selected elements of a modelling methodology involving quantization, quantum calculations and dequantization on the example of the neural model of the Day-Ahead Market of the Polish Electricity Exchange. Based on the fundamental assumptions of quantum computing, a new method has been proposed here of converting the real numbers in decimal notation into quantum mixed numbers using the probability modules of quantum mixed number and the principle of superposition, along with a new method of quantum calculations using linear algebra and vectormatrix calculus, and the Artificial Neural Network was taught accordingly. Dequantization of quantum mixed numbers to real numbers in decimal notation using the new method of dequantization has been proposed as well. The operation of the methods introduced was shown on numerical examples.
    Źródło:
    Control and Cybernetics; 2021, 50, 3; 383--399
    0324-8569
    Pojawia się w:
    Control and Cybernetics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An integrated ANN-EMO approach to reduce the risk of occupational health hazards
    Autorzy:
    Anand, Y. K.
    Srivastava, S.
    Srivastava, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/91580.pdf
    Data publikacji:
    2012
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    artificial neural network
    ANN
    evolutionary multiobjective optimisation
    EMO
    high risk of occupational health hazards
    RoOHH
    interview method
    risk assessment score
    RAS
    Opis:
    Workers in labor-intensive units, in general, maximize their earnings by subjecting themselves to high risk of occupational health hazards (RoOHH) due to economic reasons. We present an intelligent system integrating artificial neural network (ANN) and evolutionary multiobjective optimisation (EMO) to tackle this problem, which has received scant attention in the literature. A brick manufacturing unit in India is chosen as case study to demonstrate the working of proposed system. Firing is assessed to be the most severe job among others using an interview method. A job-combination approach is devised which allows firing workers to perform another job (loading/covering/molding) along with firing. The second job not only reduces their exposure to high temperature zone but also helps to compensate for reduced earnings. RoOHH is measured using a risk assessment score (RAS). ANN models the psychological responses of workers in terms of RAS, and facilitates the evaluation of a fitness function of EMO. EMO searches for optimal work schedules in a job-combination to minimize RAS and maximize earnings simultaneously. 1 Introduction Brick manufacturing (BM) in India is labor intensive and comprises the following major jobs − molding the raw bricks, loading molded bricks to kiln using a pushcart or a pony-cart, stacking molded bricks into the kiln in a particular way, spreading clay sand over the stacks uniformly for superior baking of bricks, firing the kiln that includes pouring the coal into the kiln from the covered holes at the top of the kiln at required intervals and monitoring the fire, and finally unloading the baked bricks from the kiln; we term these processes respectively as molding, loading, stacking, covering, firing and unloading, for ready references in this paper.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2012, 2, 2; 77-95
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Soft modelling of the shaping of metal profiles in rapid tube hydroforming technology
    Autorzy:
    Sadłowska, Hanna
    Kochański, Andrzej
    Powiązania:
    https://bibliotekanauki.pl/articles/29520063.pdf
    Data publikacji:
    2022
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    rapid tube hydroforming
    RTH
    manufacturing
    constitutive modelling
    soft modelling
    finite element method
    FEM
    artificial neural networks
    ANN
    hydroformowanie rur
    produkcja
    modelowanie konstytutywne
    miękkie modelowanie
    metoda elementów skończonych
    MES
    sztuczne sieci neuronowe
    Opis:
    The paper presents an approach to the impact of process parameters in innovative RTH (Rapid Tube Hydroforming) technology for shaping closed metal profiles in flexible and deformable dies. In order to implement the assumed deformation of the deformed profile, the RTH technology requires the monitoring and control of numerous technological parameters, including geometric, material, and technological variables. The paper proposes a two-stage research procedure considering hard modelling (constitutive) and soft modelling (data-driven). Due to the complexity of the technological process, it was required to develop a numerical finite element method FEM model focused on obtaining the adequate profile deformation measured by the ellipsoidality of the cylindrical profile. Based on the results of the numerical experiments, a preliminary soft mathematical model using ANN was developed. Analysing the soft model results, several statistical hypotheses were made and verified to investigate the significance of selected process parameters. Thanks to this, it was possible to select the most important process parameters, i.e., the properties of moulding sands used for RTH dies: the angle of internal friction and cohesion.
    Źródło:
    Computer Methods in Materials Science; 2022, 22, 4; 201-210
    2720-4081
    2720-3948
    Pojawia się w:
    Computer Methods in Materials Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-6 z 6

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