The applications of ANNs in certain materials-analysis problems

Kasperkiewicz, Janusz

doi:10.1016/s0924-0136(00)00641-5

Cited by 16 publications

(5 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Determining the elements of correlation matrix with factors from the interval (0, 1), the correlation matrix becomes: (8) (9) Useful statistics to check is a R 2 value of a regression fit, which measures the proportion of total variation about the mean Y śr explained by the regression and it is defined by: (10) Where both summations are over i = 1, 2,…, n. Factor R 2 is called the square of the multiple correlation coefficient and shall take the values as high as 1.…”

Section: Multiple Regression Modelmentioning

confidence: 99%

See 1 more Smart Citation

FRICTION MODELING OF Al-Mg ALLOY SHEETS BASED ON MULTIPLE REGRESSION ANALYSIS AND NEURAL NETWORKS

Lemu¹,

Trzepieciński²,

Fejkiel³

2017

Adv. Sci. Technol. Res. J.

View full text Add to dashboard Cite

This article reports a proposed approach to a frictional resistance description in sheet metal forming processes that enables the determination of the friction coefficient value under a wide range of friction conditions, without performing timeconsuming experiments. The motivation for this proposal is the fact that there exists a considerable amount of factors that affect the friction coefficient value and as a result building analytical friction model for specified process conditions is practically impossible. In this proposed approach, a mathematical model of friction behaviour is created using multiple regression analysis and artificial neural networks. The regression analysis was performed using a subroutine in MATLAB programming code and STATISTICA Neural Networks was utilized to build an artificial neural networks model. The effect of different training strategies on the quality of neural networks was studied. As input variables for regression model and training of radial basis function networks, generalized regression neural networks and multilayer networks, the results of strip drawing friction test were utilized. Four kinds of Al-Mg alloy sheets were used as a test material.

show abstract

Section: Multiple Regression Modelmentioning

confidence: 99%

“…Many researchers applied the ANN models to predict flow curves in a single step deformation on several materials [3]. An ANN may solve problems by learning rather than by a specific programming based on well-defined rules [9].…”

Section: Introductionmentioning

confidence: 99%

FRICTION MODELING OF Al-Mg ALLOY SHEETS BASED ON MULTIPLE REGRESSION ANALYSIS AND NEURAL NETWORKS

Lemu¹,

Trzepieciński²,

Fejkiel³

2017

Adv. Sci. Technol. Res. J.

View full text Add to dashboard Cite

show abstract

“…NNs have been applied widely in engineering applications such as pattern classification, function approximation, optimization, prediction, and pattern recognition [22][23][24][25][26][27][28]. In this research, the NN is used for developing a formula based on the logistic sigmoid (logsig) transfer function.…”

Section: Neural Network In Explicit Formulation Of Distortional Buckmentioning

confidence: 99%

A new formulation for distortional buckling stress in cold-formed steel members

Pala

2006

Journal of Constructional Steel Research

View full text Add to dashboard Cite

“…NNs have been applied to engineering problems, such as pattern recognition [8,9], structural optimization problems [10,11], computational and experimental mechanics [12,13], structural engineering and plasticity [14][15][16], fatigue lives of composite materials [17] and material analysis problems [18].…”

Section: Introductionmentioning

confidence: 99%