Combination predicting neural network model for E4303 electrode mechanical properties

The influence of the process parameters on the mechanical properties of compact wood powder generated via hot-pressing was analyzed through a single-factor experiment. The mechanical properties exhibited a nonlinear trend relative to the process conditions of hot-pressed compact wood powder. The relationship models between the process parameters and the mechanical properties for the compact wood powder were established by applying a multiple regression analysis and neural network methods combined with data from an orthogonal array design. A comparison between experimental and predicted results was made to investigate the accuracy of the established models by applying several data groups among the single-factor experiments. The results showed that the accuracy of the neural network model in terms of predicting the mechanical properties was greater compared with the multiple regression model. This demonstrates that the established neural network model had a better prediction performance, and it can accurately map the relationship between the process conditions and the mechanical properties of the compact wood powder.

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Prediction of Mechanical Properties of Stainless Steel Using an Artificial Neural Network Model

Reference Module in Materials Science and Materials Engineering ◽

10.1016/b978-0-12-803581-8.04148-5 ◽

2017 ◽

Cited By ~ 1

Author(s):

G.H. Senussi

Keyword(s):

Neural Network ◽

Mechanical Properties ◽

Artificial Neural Network ◽

Stainless Steel ◽

Network Model ◽

Neural Network Model ◽

Artificial Neural Network Model ◽

Artificial Neural ◽

Prediction Of Mechanical Properties

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PREDICTION OF MECHANICAL PROPERTIES OF 25CrMo48V SEAMLESS TUBE USING NEURAL NETWORK MODEL

International Journal of Modern Physics B ◽

10.1142/s0217979209060488 ◽

2009 ◽

Vol 23 (06n07) ◽

pp. 1074-1079 ◽

Cited By ~ 4

Author(s):

LAIBO SUN ◽

CHUANYOU ZHANG ◽

QINGFENG WANG ◽

MINGZHI WANG ◽

ZESHENG YAN

Keyword(s):

Neural Network ◽

Mechanical Properties ◽

Chemical Composition ◽

Network Model ◽

Neural Network Model ◽

Transverse Impact ◽

Relative Significance ◽

Line Production ◽

Prediction Of Mechanical Properties ◽

Trained Neural Network

In this investigation, a neural network model was established to predict mechanical properties of 25 CrMo 48 V seamless tubes. The sensitivity analysis was also performed to estimate the relative significance of each chemical composition in mechanical behavior of steel tubes. The results of this investigation show that there is a good agreement between experimental and predicted values indicating desirable validity of the model. Among those alloying elements, the elements of carbon, silicon and chromium tended to play a more important role in controlling both the yielding strength and the Charpy-V-Notch transverse impact toughness. In comparison, the impurities such as O , N , S and P have a relatively weak impact. More detailed dependences of mechanical properties on each chemical composition in isolation can be revealed using the established model. The well-trained neural network has a great potential in designing tough and ultrahigh-strength seamless tubes and modeling the on-line production parameters.

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