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Materials and Manufacturing Processes Volume 24, 2008 - Issue 1
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Original Articles

Artificial Neural Network (ANN)-Based Model for In Situ Prediction of Porosity of Nanostructured Porous Silicon

M. Ray School of Materials Science and Engineering, Bengal Engineering and Science University, Shibpur, Howrah, IndiaCorrespondence[email protected]
,
S. Ganguly School of Materials Science and Engineering, Bengal Engineering and Science University, Shibpur, Howrah, India
,
M. Das School of Materials Science and Engineering, Bengal Engineering and Science University, Shibpur, Howrah, India
,
S. Datta School of Materials Science and Engineering, Bengal Engineering and Science University, Shibpur, Howrah, India
,
N. R. Bandyopadhyay School of Materials Science and Engineering, Bengal Engineering and Science University, Shibpur, Howrah, India
&
S. M. Hossain Department of Physics, Bengal Engineering and Science University, Shibpur, Howrah, India
Pages 83-87 | Received 18 Jan 2008, Accepted 10 Sep 2008, Published online: 02 Mar 2009

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C. Kailasanathan & N. Selvakumar. (2016) Influence of alumina reinforcement on nano-hydroxyapatite/biopolymer composite for biomedical applications. International Journal of Polymer Analysis and Characterization 21:6, pages 554-562.
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S Datta & P P Chattopadhyay. (2013) Soft computing techniques in advancement of structural metals. International Materials Reviews 58:8, pages 475-504.
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Arushi Sharma, V. Abhimhanyu & Shubhabrata Datta. (2022) Designing hybrid PEEK composite with improved tribo-mechanical properties using neural network. Materials Today: Proceedings 68, pages 1959-1967.
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Shubrajit Bhaumik, S.D. Pathak, Swati Dey & Shubhabrata Datta. (2019) Artificial intelligence based design of multiple friction modifiers dispersed castor oil and evaluating its tribological properties. Tribology International 140, pages 105813.
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Suman Ghosal, Swati Dey, Partha Bhattacharyya, Partha Pratim Chattopadhyay & Shubhabrata Datta. (2019) Data-driven design of ternary alloy catalysts for enhanced oxide-based gas sensors’ performance. Computational Materials Science 161, pages 255-260.
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Ujjwal Ghanta & Syed Minhaz Hossain. 2019. Advances in Computer, Communication and Control. Advances in Computer, Communication and Control 385 390 .
Erhan Kayabasi, Savas Ozturk, Erdal Celik, Huseyin Kurt & Erol Arcaklioğlu. (2018) Prediction of nano etching parameters of silicon wafer for a better energy absorption with the aid of an artificial neural network. Solar Energy Materials and Solar Cells 188, pages 234-240.
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Ujjwal Ghanta, Sujay Singh, Mallar Ray, Nil Ratan Bandyopadhyay, Sambandamurthy Ganapathy & Syed Minhaz Hossain. (2017) Electrical transport through array of electrochemically etched silicon nanorods. physica status solidi (a) 214:6, pages 1600879.
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Dipankar Bhattacharyay, Duygu Kocaefe, Yasar Kocaefe & Brigitte Morais. (2015) An artificial neural network model for predicting the CO2 reactivity of carbon anodes used in the primary aluminum production. Neural Computing and Applications 28:3, pages 553-563.
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Shubhabrata Datta. 2016. Materials Design Using Computational Intelligence Techniques. Materials Design Using Computational Intelligence Techniques 39 63 .
C. Kailasanathan & N. Selvakumar. (2012) Comparative study of hydroxyapatite/gelatin composites reinforced with bio-inert ceramic particles. Ceramics International 38:5, pages 3569-3582.
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C. Kailasanathan, N. Selvakumar & Vasant Naidu. (2012) Structure and properties of titania reinforced nano-hydroxyapatite/gelatin bio-composites for bone graft materials. Ceramics International 38:1, pages 571-579.
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Mallar Ray, Nil Ratan Bandyopadhyay, Ujjwal Ghanta, Robert F. Klie, Ashit Kumar Pramanick, Samaresh Das, Samit K. Ray & Syed Minhaz Hossain. (2011) Temperature dependent photoluminescence from porous silicon nanostructures: Quantum confinement and oxide related transitions. Journal of Applied Physics 110:9.
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C. Kailasanathan, N. Selvakumar & P. Krishnaveni. (2011) Effect of Alumina Reinforcement on Nano-Hydroxyapatite/Gelatin Composite for Repairing Bone Defects. Materials and Manufacturing Processes, pages 141223092238008.
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M. Franzmeier, C. Pohl, M. Porrmann & U. Ruckert. (2004) Hardware Accelerated Data Analysis. Hardware Accelerated Data Analysis.
M. Porrmann, S. Ruping & U. Ruckert. (1999) SOM hardware with acceleration module for graphical representation of the learning process. SOM hardware with acceleration module for graphical representation of the learning process.
U. Witkowski, T. Neumann & U. Ruckert. (1999) Digital hardware realization of a hyper basis function network for on-line learning. Digital hardware realization of a hyper basis function network for on-line learning.

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