Practical aspects of the design and use of the artificial neural networks in materials engineering
Artificial neural networks are an effective and frequently used modelling method in
regression and classification tasks in the area of steels and metal alloys. New publications …
regression and classification tasks in the area of steels and metal alloys. New publications …
Hybrid identification method of coupled viscoplastic-damage constitutive parameters based on BP neural network and genetic algorithm
D Yao, Y Duan, M Li, Y Guan - Engineering Fracture Mechanics, 2021 - Elsevier
The constitutive model based on the theoretical framework of coupled viscoplastic-damage
involves calibration of multiple and high coupling parameters. The inverse calibration by …
involves calibration of multiple and high coupling parameters. The inverse calibration by …
Experimental study on influence of the temperature and composition in the steels thermo physical properties for heat transfer applications
Y Camaraza-Medina, A Hernandez-Guerrero… - Journal of Thermal …, 2022 - Springer
An experimental study is carried out to evaluate the effect of temperature and composition
on the variation of seven thermo physical properties (thermal conductivity, specific heat …
on the variation of seven thermo physical properties (thermal conductivity, specific heat …
Artificial neural networks modeling for lead removal from aqueous solutions using iron oxide nanocomposites from bio-waste mass
PL Narayana, AK Maurya, XS Wang, MR Harsha… - Environmental …, 2021 - Elsevier
Heavy metal ions in aqueous solutions are taken into account as one of the most harmful
environmental issues that ominously affect human health. Pb (II) is a common pollutant …
environmental issues that ominously affect human health. Pb (II) is a common pollutant …
Statistical and artificial neural network technique for prediction of performance in AlSi10Mg-MWCNT based composite materials
The present research paper deals with AlSi10Mg alloy/MWCNT metal matrix composite
brake pads with varying weight percentages. The Development of new brake pad materials …
brake pads with varying weight percentages. The Development of new brake pad materials …
[HTML][HTML] Machine learning approach for prediction of hydrogen environment embrittlement in austenitic steels
This study introduces a machine learning approach to predict the effect of alloying elements
and test conditions on the hydrogen environment embrittlement (HEE) index of austenitic …
and test conditions on the hydrogen environment embrittlement (HEE) index of austenitic …
Hot deformation behavior and artificial neural network modeling of β-γ TiAl alloy containing high content of Nb
G Ge, Z Wang, L Zhang, J Lin - Materials Today Communications, 2021 - Elsevier
In this work, the hot deformation behavior of high Nb-containing TiAl alloy with β+ γ phases
was investigated. Elongation, twisting and bending of the microstructure are the primary …
was investigated. Elongation, twisting and bending of the microstructure are the primary …
Optimization of process parameters for direct energy deposited Ti-6Al-4V alloy using neural networks
PL Narayana, JH Kim, J Lee, SW Choi, S Lee… - … International Journal of …, 2021 - Springer
Direct energy deposition (DED) is a highly applicable additive manufacturing (AM) method
and, therefore, widely employed in industrial repair-based applications to fabricate defect …
and, therefore, widely employed in industrial repair-based applications to fabricate defect …
Development of artificial neural networks software for arsenic adsorption from an aqueous environment
AK Maurya, M Nagamani, SW Kang, JT Yeom… - Environmental …, 2022 - Elsevier
Arsenic contamination is a global problem, as it affects the health of millions of people. For
this study, data-driven artificial neural network (ANN) software was developed to predict and …
this study, data-driven artificial neural network (ANN) software was developed to predict and …
Mixing time prediction with artificial neural network model
J Szoplik, M Ciuksza - Chemical Engineering Science, 2021 - Elsevier
The study presents the methodology for artificial neural network model learning for the
purpose of predicting mixing time on a set including 782 data depending on: type, diameter …
purpose of predicting mixing time on a set including 782 data depending on: type, diameter …