CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Neuro-fuzzy Modeling of an Eco-friendly Melting Furnace Parameters using Bio-fuels for the Agile Production of Quality Castings

by Purshottam Kumar, Ranjit Singh
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 49 - Number 22
Year of Publication: 2012
Authors: Purshottam Kumar, Ranjit Singh
10.5120/7904-1260

Purshottam Kumar, Ranjit Singh . Neuro-fuzzy Modeling of an Eco-friendly Melting Furnace Parameters using Bio-fuels for the Agile Production of Quality Castings. International Journal of Computer Applications. 49, 22 ( July 2012), 25-32. DOI=10.5120/7904-1260

@article{ 10.5120/7904-1260,
author = { Purshottam Kumar, Ranjit Singh },
title = { Neuro-fuzzy Modeling of an Eco-friendly Melting Furnace Parameters using Bio-fuels for the Agile Production of Quality Castings },
journal = { International Journal of Computer Applications },
issue_date = { July 2012 },
volume = { 49 },
number = { 22 },
month = { July },
year = { 2012 },
issn = { 0975-8887 },
pages = { 25-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume49/number22/7904-1260/ },
doi = { 10.5120/7904-1260 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:47:01.906823+05:30
%A Purshottam Kumar
%A Ranjit Singh
%T Neuro-fuzzy Modeling of an Eco-friendly Melting Furnace Parameters using Bio-fuels for the Agile Production of Quality Castings
%J International Journal of Computer Applications
%@ 0975-8887
%V 49
%N 22
%P 25-32
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper neuro-fuzzy technique is used for the first time in modeling eco-friendly furnace parameters to predict the melting rate of the molten metal required to produce homogenous and quality castings. The relationship between the process variables (input) viz. flame temperature, preheat air temperature, rotational speed of the furnace dome, percentage of excess air, melting time, fuel consumption and melting rate (output) is very complex and is agreeable to neuro-fuzzy approach. The neuro-fuzzy model has been developed out of training data obtained from the series of experimentation carried out on eco-friendly self designed and developed 200 kg capacity rotary furnace using bio-fuels. The results provided by neuro-fuzzy model compares well with the experimental data. This work has considerable implications in selection and control of process variables in real time and ability to achieve energy and material savings, quality improvement and development of homogeneous properties throughout the casting and is a step towards agile manufacturing.

References
  1. S. L. Goldman, R. N. Nagel, and K. Preiss, "Agile Competitors and Virtual Organizations: Strategies for Enriching the Customer", Van Nostrand Reinhold, New York, 1995.
  2. Y. Y. Yusuf, M. Sarhadi, and A. Gunasekaran, "Agile Manufacturing: The Drivers, Concepts and Attributes", International Journal of Production Economics, Vol. 62, 1999, pp. 33-43.
  3. P. T. Kidd, "Agile Manufacturing: Forging New Frontiers", Addison - Wesley Reading, M. A. , 1994.
  4. R. Singh, G. Das, and R. Setia, "Parametric Modeling of a Rotary Furnace for Agile Production of Castings with Artificial Neural Networks," International Journal of Agile Manufacturing, Vol. 10, Issue 2, 2007, pp. 137–147.
  5. H. R. Berenji, and P. Khedkar, "Learning and Tuning Fuzzy Logic Controllers Through Reinforcements", IEEE Trans. Neural Networks, Vol. 3(5), 1992, pp. 724-740.
  6. J. J. Buckley, and Y. Hayashi, "Fuzzy Neural Networks: A Survey", Fuzzy Sets and Systems, Vol. 66, 1994, pp. 1-13.
  7. J. J. Buckley, and Y. Hayashi, "Neural Networks for Fuzzy Systems", Fuzzy Sets and Systems, Vol. 71, 1992, pp. 265-276.
  8. J. S. R. Jang, "ANFIS: Adaptive-Network-Based Fuzzy Inference Systems" IEEE Transaction on Systems, Man & Cybernetics, Vol. 23, 1993, pp. 665-685.
  9. T. Takagi, and M. Sugeno, "Fuzzy Identification of Systems and its Application to Modeling and Control", IEEE Transaction on Systems, Man & Cybernetics, Vol. 15, 1985, pp. 116-132.
  10. S. K. Halgamuge, and M. Glesner, "Neural Networks in Designing Fuzzy Systems for Real World Applications", Fuzzy Sets and Systems, Vol. 65, 1994, pp. 1-12.
  11. D. Nauck, and K. Kruse, "Designing Neuro - fuzzy Systems Through Back - propagation". In Witold Pedrycz, editor, Fuzzy Modelling: Paradigms and Practice, 1996, pp. 203-228, Kluwer, Boston.
  12. D. Nauck, and K. Kruse, "Neuro-fuzzy Systems Research and Applications Outside of Japan" (in Japanese), Fuzzy-Neural Networks (in Japanese), Soft Computing Series, 1996, pp. 108-134, Asakura Publications, Tokyo.
  13. S. K. Halgamuge, and M. Glesner, "Neural Networks in Designing Fuzzy Systems for Real World Applications," Fuzzy Sets and Systems, Vol. 65, 1994, pp. 1-12.
  14. R. Singh, R. Setia, and G. Das, "Modeling and Optimization of Rotary Furnace Parameters using Artificial Neural Networks and Genetic Evolutionary Algorithms," Proceedings of 31st National Systems Conference, MIT, Manipal, December 14–15, 2007, P. No. –75.
  15. R. Singh, G. Das, and R. K. Jain, "Modeling and Optimization of Rotary Furnace Parameters using Regression and Numerical Techniques," Proceedings of 68th World Foundry Congress & 56th Indian Foundry Congress, Chennai Trade Centre, Chennai, February 7–10, 2008, P. No. OP-62, pp. 333-339.
  16. M. Sugeno, and G. T. Kang, "Structure Identification of Fuzzy Model", Fuzzy Sets and Systems, Vol. 28, 1998, pp. 15-33.
Index Terms

Computer Science
Information Sciences

Keywords

Neuro-Fuzzy Rotary Furnace Bio-fuel Artificial Neural Network (ANN) Adaptive Network - based Fuzzy Inference System (ANFIS) Agile Manufacturing Systems (AMS)

Powered by PhDFocusTM