Prediction of temperature-frequency-dependent mechanical properties of composites based on thermoplastic liquid resin reinforced with carbon fibers using artificial neural networks

2019 ◽  
Vol 105 (5-6) ◽  
pp. 2543-2556 ◽  
Author(s):  
Lorena Cristina Miranda Barbosa ◽  
Guilherme Gomes ◽  
Antonio Carlos Ancelotti Junior
Keyword(s):  
Mechanical Properties ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Carbon Fibers ◽  
Frequency Dependent ◽  
Liquid Resin ◽  
Artificial Neural ◽  
Properties Of Composites

Prediction of Ferrite-Martensite Dual-Phase Steels Mechanical Properties by Use of Artificial Neural Networks

2013 ◽  
Vol 773-774 ◽  
pp. 268-274
Author(s):  
Amir Ghiami ◽  
Ramin Khamedi
Keyword(s):  
Mechanical Properties ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Back Propagation ◽  
Total Elongation ◽  
Ultimate Tensile Stress ◽  
Dual Phase ◽  
Dual Phase Steels ◽  
Good Ability ◽  
Artificial Neural

This paper presents an investigation of the capabilities of artificial neural networks (ANN) in predicting some mechanical properties of Ferrite-Martensite dual-phase steels applicable for different industries like auto-making. Using ANNs instead of different destructive and non-destructive tests to determine the material properties, reduces costs and reduces the need for special testing facilities. Networks were trained with use of a back propagation (BP) error algorithm. In order to provide data for training the ANNs, mechanical properties, inter-critical annealing temperature and information about the microstructures of many specimens were examined. After the ANNs were trained, the four parameters of yield stress, ultimate tensile stress, total elongation and the work hardening exponent were simulated. Finally a comparison of the predicted and experimental values indicates that the results obtained from the given input data reveal a good ability of the well-trained ANN to predict the described mechanical properties.

Artificial Neural Networks for the Prediction of Mechanical Properties of Soils

2021 ◽  
pp. 758-779
Author(s):  
Lusdali Castillo Delgado ◽  
Daniel Enrique Porta Maldonado ◽  
Juan J. Soria ◽  
Leopoldo Choque Flores
Keyword(s):  
Mechanical Properties ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Artificial Neural ◽  
Prediction Of Mechanical Properties

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%.

Experimental study on the 3D-printed plastic parts and predicting the mechanical properties using artificial neural networks

2016 ◽  
Vol 28 (8) ◽  
pp. 1044-1051 ◽  
Author(s):  
Ömer Bayraktar ◽  
Gültekin Uzun ◽  
Ramazan Çakiroğlu ◽  
Abdulmecit Guldas
Keyword(s):  
Mechanical Properties ◽  
Neural Networks ◽  
Experimental Study ◽  
Artificial Neural Networks ◽  
3D Printed ◽  
Artificial Neural ◽  
Plastic Parts
Sign in / Sign up

Export Citation Format

Share Document