Prediction of Fluidity of Casting Aluminum Alloys Using Artificial Neural Network

2019 ◽  
Author(s):  
Y. Gao ◽  
H. Liao ◽  
X. Suo ◽  
Q. Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Aluminum Alloys ◽  
Artificial Neural ◽  
Casting Aluminum

scholarly journals Prediction of Mechanical Properties by Artificial Neural Networks to Characterize the Plastic Behavior of Aluminum Alloys

Materials ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 5227
Author(s):  
David Merayo ◽  
Alvaro Rodríguez-Prieto ◽  
Ana María Camacho
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Aluminum Alloys ◽  
Food Packaging ◽  
Relevant Information ◽  
Plastic Behavior ◽  
Artificial Neural

In metal forming, the plastic behavior of metallic alloys is directly related to their formability, and it has been traditionally characterized by simplified models of the flow curves, especially in the analysis by finite element simulation and analytical methods. Tools based on artificial neural networks have shown high potential for predicting the behavior and properties of industrial components. Aluminum alloys are among the most broadly used materials in challenging industries such as aerospace, automotive, or food packaging. In this study, a computer-aided tool is developed to predict two of the most useful mechanical properties of metallic materials to characterize the plastic behavior, yield strength and ultimate tensile strength. These prognostics are based on the alloy chemical composition, tempers, and Brinell hardness. In this study, a material database is employed to train an artificial neural network that is able to make predictions with a confidence greater than 95%. It is also shown that this methodology achieves a performance similar to that of empirical equations developed expressly for a specific material, but it provides greater generality since it can approximate the properties of any aluminum alloy. The methodology is based on the usage of artificial neural networks supported by a big data collection about the properties of thousands of commercial materials. Thus, the input data go above 2000 entries. When the relevant information has been collected and organized, an artificial neural network is defined, and after the training, the artificial intelligence is able to make predictions about the material properties with an average confidence greater than 95%.

Research on Corrosion Rate Prediction of Aluminum Alloys in Typical Domestic Areas Based on BP Artificial Neural Network

2013 ◽  
Vol 652-654 ◽  
pp. 1088-1091 ◽  
Author(s):  
Li Li ◽  
Peng Qiu ◽  
Shi Bo Xing ◽  
Xiao Su
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Aluminum Alloy ◽  
Aluminum Alloys ◽  
Corrosion Test ◽  
Atmospheric Corrosion ◽  
Climatic Factors ◽  
Test Results ◽  
Bp Artificial Neural Network ◽  
Artificial Neural

By analyzing the climatic factors and aluminum alloys corrosion data in 10 atmospheric corrosion sites, the aluminum alloy atmospheric corrosion prediction model was built. The reasonableness of the corrosion model was verified by using the BP artificial neural network to learn, train, simulate, and compare with the corrosion test results of aluminum alloy samples in 10 typical atmospheric corrosion test stations. The results show that a stable forecasting model can be built based on the BP artificial neural network, which well predicted the corrosion rates of aluminum alloys in 10 typical atmospheric corrosion test stations.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural

Forecasting travel demand: a comparison of logit and artificial neural network methods

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Travel Demand ◽  
Network Methods ◽  
Artificial Neural
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