Artificial neural network modeling for surface roughness prediction in cylindrical grinding of Al-SiCp metal matrix composites and ANOVA analysis

Advances in Production Engineering & Management ◽

10.14743/apem2014.2.176 ◽

2014 ◽

Vol 9 (2) ◽

pp. 59-70 ◽

Cited By ~ 13

Author(s):

M. Chandrasekaran ◽

D. Devarasiddappa

Keyword(s):

Neural Network ◽

Surface Roughness ◽

Artificial Neural Network ◽

Metal Matrix ◽

Network Modeling ◽

Neural Network Modeling ◽

Matrix Composites ◽

Anova Analysis ◽

Artificial Neural Network Modeling ◽

Roughness Prediction

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Artificial Neural Network Modeling for Predicting Surface Roughness in End Milling of Al-SiCp Metal Matrix Composites and Its Evaluation

Journal of Applied Sciences ◽

10.3923/jas.2012.955.962 ◽

2012 ◽

Vol 12 (10) ◽

pp. 955-962 ◽

Cited By ~ 8

Author(s):

D. Devarasidd ◽

M. Chandrasek ◽

Amitava Mandal

Keyword(s):

Neural Network ◽

Surface Roughness ◽

Artificial Neural Network ◽

Metal Matrix Composites ◽

Metal Matrix ◽

End Milling ◽

Network Modeling ◽

Neural Network Modeling ◽

Matrix Composites ◽

Artificial Neural Network Modeling

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Artificial Neural Network Modeling for Predicting Surface Roughness and Material Removal Rate in End Milling of AA7075-SiC-Al2O3 Hybrid Metal Matrix Composites

Asian Journal of Research in Social Sciences and Humanities ◽

10.5958/2249-7315.2016.01293.4 ◽

2016 ◽

Vol 6 (12) ◽

pp. 296

Author(s):

B Rajeswari ◽

K S Amirthagadeswaran

Keyword(s):

Neural Network ◽

Surface Roughness ◽

Metal Matrix ◽

Material Removal ◽

End Milling ◽

Removal Rate ◽

Network Modeling ◽

Neural Network Modeling ◽

Matrix Composites ◽

Artificial Neural Network Modeling

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Artificial Neural Network Modeling of Surface Roughness in Magnetic Abrasive Finishing Process

Research Journal of Applied Sciences Engineering and Technology ◽

10.19026/rjaset.6.3812 ◽

2013 ◽

Vol 6 (11) ◽

pp. 1976-1983 ◽

Cited By ~ 4

Author(s):

F. Djavanroodi

Keyword(s):

Neural Network ◽

Surface Roughness ◽

Artificial Neural Network ◽

Network Modeling ◽

Neural Network Modeling ◽

Magnetic Abrasive Finishing ◽

Artificial Neural Network Modeling ◽

Finishing Process ◽

Abrasive Finishing ◽

Artificial Neural

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Comment on “Artificial neural network modeling of mechanical alloying process for synthesizing of metal matrix nanocomposite powders” by Dashtbayazi et al. [Mater. Sci. Eng. A 466 (2007) 274]

Materials Science and Engineering A ◽

10.1016/j.msea.2008.03.024 ◽

2008 ◽

Vol 492 (1-2) ◽

pp. 491-492 ◽

Cited By ~ 3

Author(s):

W. Sha

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Mechanical Alloying ◽

Metal Matrix ◽

Network Modeling ◽

Neural Network Modeling ◽

Artificial Neural Network Modeling ◽

Metal Matrix Nanocomposite ◽

Nanocomposite Powders ◽

Matrix Nanocomposite

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Artificial neural network modeling of mechanical alloying process for synthesizing of metal matrix nanocomposite powders

Materials Science and Engineering A ◽

10.1016/j.msea.2007.02.075 ◽

2007 ◽

Vol 466 (1-2) ◽

pp. 274-283 ◽

Cited By ~ 33

Author(s):

M.R. Dashtbayazi ◽

A. Shokuhfar ◽

A. Simchi

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Mechanical Alloying ◽

Metal Matrix ◽

Network Modeling ◽

Neural Network Modeling ◽

Artificial Neural Network Modeling ◽

Metal Matrix Nanocomposite ◽

Nanocomposite Powders ◽

Matrix Nanocomposite

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Artificial Neural Network Modeling of Pavement Performance using Expert Judgement

Road Materials and Pavement Design ◽

10.3166/rmpd.3.373-384 ◽

2002 ◽

Vol 3 (4) ◽

pp. 373-384

Author(s):

Medhi Parvani

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Network Modeling ◽

Pavement Performance ◽

Expert Judgement ◽

Neural Network Modeling ◽

Artificial Neural Network Modeling ◽

Artificial Neural

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Artificial Neural Network Modeling of Phytoestrogen Binding to Estrogen Receptors

Letters in Drug Design & Discovery ◽

10.2174/157018006778194871 ◽

2006 ◽

Vol 3 (7) ◽

pp. 436-442 ◽

Cited By ~ 4

Author(s):

S. Agatonovic-Kustrin ◽

J. Turner

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Estrogen Receptors ◽

Network Modeling ◽

Neural Network Modeling ◽

Artificial Neural Network Modeling ◽

Artificial Neural

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An Artificial Neural Network Modeling to Study Unpredictable Degradation of Carbon Steel Marine Structure with Environmental Variables: Chloride, Sulphate, Bicarbonates, pH

Innovations in Corrosion and Materials Science (Formerly Recent Patents on Corrosion Science) ◽

10.2174/2352094904999141202103233 ◽

2015 ◽

Vol 4 (2) ◽

pp. 127-133

Author(s):

Subir Paul ◽

Sujit K Guchhait ◽

Kamal Goswami

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Carbon Steel ◽

Environmental Variables ◽

Network Modeling ◽

Neural Network Modeling ◽

Artificial Neural Network Modeling ◽

Marine Structure ◽

Artificial Neural

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Porosity Estimation of Mesoporous TiO 2 ‐ZnO Nanocrystalline by Artificial Neural Network Modeling

Chemical Engineering & Technology ◽

10.1002/ceat.202000297 ◽

2021 ◽

Author(s):

Soroush Soltani ◽

Taha Roodbar Shojaei ◽

Nasrin Khanian ◽

Thomas Shean Yaw Choong ◽

Nilofar Asim ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Network Modeling ◽

Neural Network Modeling ◽

Artificial Neural Network Modeling ◽

Artificial Neural ◽

Porosity Estimation

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Heat induction in two-dimensional graphene–Fe3O4 nanohybrids for magnetic hyperthermia applications with artificial neural network modeling

RSC Advances ◽

10.1039/d1ra03428f ◽

2021 ◽

Vol 11 (35) ◽

pp. 21702-21715

Author(s):

M. S. Dar ◽

Khush Bakhat Akram ◽

Ayesha Sohail ◽

Fatima Arif ◽

Fatemeh Zabihi ◽

...

Keyword(s):

Neural Network ◽

Magnetic Field ◽

Artificial Neural Network ◽

Network Modeling ◽

Magnetic Hyperthermia ◽

Alternating Magnetic Field ◽

Neural Network Modeling ◽

Two Dimensional ◽

Artificial Neural Network Modeling ◽

Heat Induction

Synthesis of Fe3O4–graphene (FG) nanohybrids and magnetothermal measurements of FxG100–x (x = 0, 25, 45, 65, 75, 85, 100) nanohybrids (25 mg each) at a 633 kHz alternating magnetic field of strength 9.1 mT.

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