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Artificial neural networks for predicting potentiodynamic tests of brass 70-30
Materials Today: Proceedings,
2023
DOI:10.1016/j.matpr.2023.01.287
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Artificial neural networks for predicting potentiodynamic tests of brass 70-30
Materials Today: Proceedings,
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DOI:10.1016/j.matpr.2023.01.287
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Predicting the Corrosion Rate of Medium Carbon Steel Using Artificial Neural Networks
Protection of Metals and Physical Chemistry of Surfaces,
2022
DOI:10.1134/S2070205122020034
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The Role of Artificial Neural Networks in Prediction of Mechanical and Tribological Properties of Composites—A Comprehensive Review
Archives of Computational Methods in Engineering,
2022
DOI:10.1007/s11831-021-09691-7
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The effect of alpha-lithium aluminate incorporation on the properties of bovine bone-derived hydroxyapatite
Journal of the Australian Ceramic Society,
2022
DOI:10.1007/s41779-022-00796-1
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Artificial neural network based prediction on tribological properties of polycarbonate composites reinforced with graphene and boron carbide particle
Materials Today: Proceedings,
2020
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Artificial neural network based prediction on tribological properties of polycarbonate composites reinforced with graphene and boron carbide particle
Materials Today: Proceedings,
2020
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DC and AC conductivity properties of bovine dentine hydroxyapatite (BDHA)
IOP Conference Series: Materials Science and Engineering,
2017
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Titanium implants with modified surfaces: Meta-analysis of in vivo osteointegration
Materials Science and Engineering: C,
2015
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The Effect of Yttrium Oxide Reinforcement on the Microstructural and Mechanical Properties of Biologically Derived Hydroxyapatite
Acta Physica Polonica A,
2015
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Reduction of Biofilm Infection Risks and Promotion of Osteointegration for Optimized Surfaces of Titanium Implants
Advanced Healthcare Materials,
2012
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Reduction of Biofilm Infection Risks and Promotion of Osteointegration for Optimized Surfaces of Titanium Implants
Advanced Healthcare Materials,
2012
DOI:10.1002/adhm.201100006
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