Temat: artificial neural network design - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: ANN model of stress-strain relationship for aluminium sponge in uniaxial compression
Autorzy:: Dudzik, Marek
Stręk, Anna Małgorzata
Powiązania:: https://bibliotekanauki.pl/articles/1839632.pdf
Data publikacji:: 2020
Wydawca:: Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej
Tematy:: artificial neural network design
compressive behaviour
open-cell aluminium
model equation
Opis:: In this article, we present a proposition of a model of the compressive behaviour of open- -cell aluminium with relation to the material apparent density. The research was based on experimental data from uniaxial compression tests conducted for two sample lots. These results were analysed with the use of neural networks in a specially designed algorithm. The main criterion for choosing a satisfactory approximation was mean absolute relative error MARE<5%. As a result of the analysis, the sought relation was extracted and is presented as a proposition of a new ANN model of the compressive stress-strain relationship for aluminium sponge.
Źródło:: Journal of Theoretical and Applied Mechanics; 2020, 58, 2; 385-390
1429-2955
Pojawia się w:: Journal of Theoretical and Applied Mechanics
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Prediction of the Density of Energetic Co-crystals: a Way to Design High Performance Energetic Materials
Autorzy:: Zohari, Narges
Mohammadkhani, Faezeh Ghiasvand
Powiązania:: https://bibliotekanauki.pl/articles/357956.pdf
Data publikacji:: 2020
Wydawca:: Sieć Badawcza Łukasiewicz - Instytut Przemysłu Organicznego
Tematy:: energetic co-crystals
density
MLR method
artificial neural network
molecular design
Opis:: For designing a new energetic material with good performance, a knowledge of its density is required. In this study, the relationship between the densities of energetic co-crystals and their molecular structures was examined through a quantitative structure-property relationship (QSPR) method. The methodology of this research provides a new model which can relate the density of an energetic co-crystal to several molecular structural descriptors, which are calculated by Dragon software. It is indicated that the density of a co-crystal is a function of sp, OB, DU, nAT, as well as several non-additive structural parameters. The new recommended correlation was derived on the basis of the experimental densities of 50 co-crystals with various structures as a training set. The R2 or determination coefficient of the derived correlation was 0.937. This correlation provided a suitable estimate for a further 12 energetic co-crystals as a test set. Meanwhile, the predictive ability of the correlation was investigated through a cross validation method. Moreover, the new model has more reliability and performance for predicting the densities of energetic co-crystals compared to a previous one which was based on an artificial neural network approach. As a matter of fact, designing novel energetic co-crystals is possible by utilising the proposed method.
Źródło:: Central European Journal of Energetic Materials; 2020, 17, 1; 31-48
1733-7178
Pojawia się w:: Central European Journal of Energetic Materials
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Experimental and numerical investigation of the deep drawing process for an automobile panel and prediction of appropriate amount of parameters by multi-layer neural network
Autorzy:: Najafabadi, S. S.
Anaraki, A. T.
Moradi, M.
Powiązania:: https://bibliotekanauki.pl/articles/281868.pdf
Data publikacji:: 2017
Wydawca:: Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej
Tematy:: deep drawing
finite element analysis (FEA)
multi-layer artificial neural network (ANN)
Taguchi design
Opis:: In this paper, the deep drawing process of an automobile panel in order to select the appropriate amount of parameters has been investigated. The parameters include friction between the blank and die, blank width and length, blank thickness and gap between the blank and blank-holder. A multi-layer artificial neural network (ANN) trained by finite element analyses (FEA) is applied in order to improve forming parameters and achieve a better quality. As the FEA results are used to train the ANN, the FEA results have been verified by three experiments. Finally, an appropriate amount of each parameter is predicted by the trained ANN and a FEA has been done based on the ANN prediction to evaluate the accuracy of the trained ANN. Moreover, it is shown that the ANN could predict results within a 10 percent error. In addition, the proposed method for prediction of the appropriate parameters (ANN) is confirmed by comparing with the Taguchi design of experiment prediction. It is also shown that the model obtained by the former method has lower errors than the latter one. In this study, the Taguchi model is used to evaluate the effect of parameters on tearing and wrinkling. Based on the Taguchi design of experiment, while the blank length is the most effective parameter on tearing, the maximum height of wrinkles on flanged parts mainly depends on the blank thickness.
Źródło:: Journal of Theoretical and Applied Mechanics; 2017, 55, 2; 707-718
1429-2955
Pojawia się w:: Journal of Theoretical and Applied Mechanics
Dostawca treści:: Biblioteka Nauki

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