Cutting force prediction when green machining of thin-walled Ti-6Al-4V under dry and MQL-cutting using response surface methodology and artificial neural networks-algorithm

Mapping Intimacies ◽

10.1063/1.5118035 ◽

2019 ◽

Author(s):

M. Yanis ◽

A. S. Mohruni ◽

S. Sharif ◽

I. Yani ◽

Z. Suzen ◽

...

Keyword(s):

Neural Networks ◽

Response Surface Methodology ◽

Artificial Neural Networks ◽

Cutting Force ◽

Response Surface ◽

Cutting Force Prediction ◽

Force Prediction ◽

Green Machining ◽

Thin Walled ◽

Artificial Neural

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Modeling and optimization of pectin extraction from banana peel using artificial neural networks (ANNs) and response surface methodology (RSM)

Journal of Food Measurement & Characterization ◽

10.1007/s11694-021-00852-7 ◽

2021 ◽

Author(s):

Ermias Girma Aklilu

Keyword(s):

Neural Networks ◽

Response Surface Methodology ◽

Artificial Neural Networks ◽

Response Surface ◽

Banana Peel ◽

Modeling And Optimization ◽

Artificial Neural

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Optimization of hypochlorous acid generation by HCl electrolysis through response surface methodology and artificial neural networks

Journal of Environmental Chemical Engineering ◽

10.1016/j.jece.2021.105826 ◽

2021 ◽

pp. 105826

Author(s):

Muhammad Yaqub ◽

Changyeon Woo ◽

Wontae Lee

Keyword(s):

Neural Networks ◽

Response Surface Methodology ◽

Artificial Neural Networks ◽

Response Surface ◽

Hypochlorous Acid ◽

Acid Generation ◽

Artificial Neural

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Simultaneous extraction of polysaccharides and polyphenols from blackcurrant fruits: Comparison between response surface methodology and artificial neural networks

Industrial Crops and Products ◽

10.1016/j.indcrop.2021.113682 ◽

2021 ◽

Vol 170 ◽

pp. 113682

Author(s):

Xueying Bu ◽

Yaqin Xu ◽

Meimei Zhao ◽

Dalong Li ◽

Jiaheng Zou ◽

...

Keyword(s):

Neural Networks ◽

Response Surface Methodology ◽

Artificial Neural Networks ◽

Response Surface ◽

Simultaneous Extraction ◽

Artificial Neural

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Comparative assessment of response surface methodology and artificial neural networks in forecasting biodiesel yield from waste cooking sunflower oil

Materials Today Proceedings ◽

10.1016/j.matpr.2021.02.291 ◽

2021 ◽

Author(s):

B. Devaraj Naik ◽

M. Udayakumar

Keyword(s):

Neural Networks ◽

Response Surface Methodology ◽

Artificial Neural Networks ◽

Response Surface ◽

Sunflower Oil ◽

Comparative Assessment ◽

Artificial Neural

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Photocatalytic oxidation of treated municipal wastewaters for the removal of phenolic compounds: optimization and modeling using response surface methodology (RSM) and artificial neural networks (ANNs)

Journal of Chemical Technology & Biotechnology ◽

10.1002/jctb.3755 ◽

2012 ◽

Vol 87 (10) ◽

pp. 1385-1395 ◽

Cited By ~ 33

Author(s):

Maria Antonopoulou ◽

Vassilios Papadopoulos ◽

Ioannis Konstantinou

Keyword(s):

Neural Networks ◽

Response Surface Methodology ◽

Artificial Neural Networks ◽

Phenolic Compounds ◽

Response Surface ◽

Photocatalytic Oxidation ◽

Artificial Neural ◽

Municipal Wastewaters

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Optimizing Wear Behavior of Epoxy Composites Using Response Surface Methodology and Artificial Neural Networks

Polymer Composites ◽

10.1002/pc.25089 ◽

2018 ◽

Vol 40 (7) ◽

pp. 2812-2818

Author(s):

D. Satish Kumar ◽

M. Rajmohan

Keyword(s):

Neural Networks ◽

Response Surface Methodology ◽

Artificial Neural Networks ◽

Response Surface ◽

Wear Behavior ◽

Epoxy Composites ◽

Artificial Neural

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Modeling the Performance of Vortex Tube using Response Surface Methodology and Artificial Neural Networks

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/390/1/012010 ◽

2018 ◽

Vol 390 ◽

pp. 012010 ◽

Cited By ~ 3

Author(s):

G Suresh Kumar ◽

B Veerabhadra Reddy ◽

G Sankaraiah ◽

P Venkateshwar Reddy

Keyword(s):

Neural Networks ◽

Response Surface Methodology ◽

Artificial Neural Networks ◽

Response Surface ◽

Vortex Tube ◽

Artificial Neural

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Enhanced removal of Cr(VI) by cerium oxide polyaniline composite: Optimization and modeling approach using response surface methodology and artificial neural networks

Journal of Environmental Chemical Engineering ◽

10.1016/j.jece.2015.03.028 ◽

2015 ◽

Vol 3 (2) ◽

pp. 870-885 ◽

Cited By ~ 21

Author(s):

S. Mandal ◽

S.S. Mahapatra ◽

R.K. Patel

Keyword(s):

Neural Networks ◽

Response Surface Methodology ◽

Artificial Neural Networks ◽

Response Surface ◽

Cerium Oxide ◽

Composite Optimization ◽

Modeling Approach ◽

Artificial Neural

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Modeling the calcium and magnesium removal from seawater by immobilized biomass of ureolytic bacteria Bacillus subtilis through response surface methodology and artificial neural networks

10.5004/dwt.2018.22665 ◽

2018 ◽

Vol 118 ◽

pp. 294-303 ◽

Cited By ~ 3

Author(s):

Dayana Arias ◽

Mariella Rivas ◽

Ricardo Guiñez ◽

Luis A. Cisternas

Keyword(s):

Neural Networks ◽

Response Surface Methodology ◽

Bacillus Subtilis ◽

Artificial Neural Networks ◽

Response Surface ◽

Immobilized Biomass ◽

Calcium And Magnesium ◽

Artificial Neural ◽

Magnesium Removal ◽

Ureolytic Bacteria

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Comparison of Artificial Neural Networks (ANN) and Response Surface Methodology (RSM) Modeling Approaches in Predicting the Deposition Efficiency of Plasma Sprayed Alumina Coatings on AZ31B Magnesium Alloy

Journal of Advanced Microscopy Research ◽

10.1166/jamr.2017.1317 ◽

2017 ◽

Vol 12 (1) ◽

pp. 40-49 ◽

Cited By ~ 2

Author(s):

D. Thirumalaikumarasamy ◽

V. Balasubramanian ◽

S. Sree Sabari ◽

S. Vignesh

Keyword(s):

Neural Networks ◽

Response Surface Methodology ◽

Artificial Neural Networks ◽

Magnesium Alloy ◽

Response Surface ◽

Deposition Efficiency ◽

Alumina Coatings ◽

Az31b Magnesium Alloy ◽

Artificial Neural ◽

Plasma Sprayed

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