scholarly journals The use of artificial neural networks for mathematical modeling of the effect of composition and production conditions on the properties of PVC floor coverings

2017 ◽  
Vol 71 (1) ◽  
pp. 11-18
Author(s):  
Rajko Radovanovic ◽  
Mirjana Jovicic ◽  
Oskar Bera ◽  
Jelena Pavlicevic ◽  
Branka Pilic ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Processing Conditions ◽  
Complex Composition ◽  
Strongly Connected ◽  
Artificial Neural ◽  
Floor Coverings ◽  
End Use ◽  
Hidden Neurons

The application of PVC floor coverings is strongly connected with their end-use properties, which depend on the composition and processing conditions. It is very difficult to estimate the proper influence of the production parameters on the characteristics of PVC floor coverings due to their complex composition and various preparation procedures. The effect of different processing variables (such as time of bowling, temperature of bowling and composition of PVC plastisol) on the mechanical properties of PVC floor coverings was investigated. The influence of different input parameters on the mechanical properties was successfully determined using an artificial neural network with an optimized number of hidden neurons. The Garson and Yoon models were applied to calculate and describe the variable contributions in the artificial neural networks.

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

Towards an Improved Ensemble Learning Model of Artificial Neural Networks

2016 ◽  
pp. 762-793
Author(s):  
Fatai Anifowose ◽  
Jane Labadin ◽  
Abdulazeez Abdulraheem
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Reservoir Characterization ◽  
Evaluation Criteria ◽  
Search Space ◽  
Combination Rules ◽  
Search Spaces ◽  
Ensemble Machine Learning ◽  
Artificial Neural ◽  
Hidden Neurons

Artificial Neural Networks (ANN) have been widely applied in petroleum reservoir characterization. Despite their wide use, they are very unstable in terms of performance. Ensemble machine learning is capable of improving the performance of such unstable techniques. One of the challenges of using ANN is choosing the appropriate number of hidden neurons. Previous studies have proposed ANN ensemble models with a maximum of 50 hidden neurons in the search space thereby leaving rooms for further improvement. This chapter presents extended versions of those studies with increased search spaces using a linear search and randomized assignment of the number of hidden neurons. Using standard model evaluation criteria and novel ensemble combination rules, the results of this study suggest that having a large number of “unbiased” randomized guesses of the number of hidden neurons beyond 50 performs better than very few occurrences of those that were optimally determined.

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%.

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