Determining the Compressive Strength of Green Sand Moulds with Different Moisture Contents, Using Artificial Neural Networks

Document Type : Research Paper

Authors

• R. Meshkabadi
• Gh. R Marami
• K. Jahani

Abstract

The quality of parts in green sand mold casting mostly depends on the green sand properties such as compressive strength, permeability, mould hardness, etc. These parameters depend on input parameters like water, size and shape of the sand, percentage of bentonite and so on. The compressive strength of sand of different moisture contents which is required for modeling, obtained by experimental tests, and a research method, based on Artificial Neural Network (ANN). The predicted values of the compressive strength obtained by the ANN and new experiments found to be in good agreement with each other and showed that by using Artificial Neural Networks one can estimate the compressive strength of green sand precisely before molding. 

Keywords

• Green Sand Mould
• Artificial Neural Networks
• Green Sand Compressive Strength

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References

1- K.G. Swift and J.D. Booker, Casting

Processes, Manufacturing Process

Selection Handbook, pp. 61-91, 2013.

2- Y. Chang, and H. Hocheng, "The

flow ability of moldings sand", Journal

of Materials Processing Technology,

Vol.113, pp. 238- 244, 2001.

3- R. R. Kundu, and B.N. Lahiri,

"Study and Statistical Modeling of

Green Sand Mould Properties Using

RSM", International Journal of

Materials and Product Technology,

Vol. 31, No. 2-4, pp.143-158, 2008.

4- Benny Karunakar, D., Datta, and

G.L., "Controlling Green Sand Mould

Properties Using Artificial Neural

Networks and Genetic Algorithms - A

Comparison ", Applied Clay Science,

Vol. 37, No. 1-2, pp. 58-66. , 2007.

5- N. Nagurbabu, R.K.Ohdar, and P.T.

Push, "Application of Intelligent

Techniques for Controlling the Green

Sand Properties ", Proceeding of 55th

Indian Foundry Congress, pp.178-186,

2007.

6- M. B. Parappagoudar, D.K. Pratihar,

and G. L. Datta, "Forward and reverse

Mappings in Green Sand Mould

System Using Neural Networks",

Applied Soft Computing, Vol.8,

pp.239–260, 2008.

7- D. B. Karunakar, and G.L. Datta,

"Modeling and Optimization of Green

Sand Mould Parameters Using Genetic

Algorithms", Transactions of Institute

of Indian Foundry Men, Vol.51, pp.

262–267, 2003a.

8- D. B. Karunakar, and G.L. Datta,

"Modeling of green sand mould

parameters using artificial neural

networks", Indian Foundry Journal,

Vol.49, No.12, pp. 27–36, 2003b.

9 -ح. مناجاتی زاده، د. آصفی، ا. قدمیار، م. م. عقیده، ا.

انصاری پور و احمد رضا سـلیمی، " بررسـی و تحلیـل

تأثیر شرایط تولید بر مقاومت به ضربه فولادهای میکرو

آلیاژی مورد استفاده در لوله سازی با استفاده از شبکه

عصبی،" مجله علمی – پژوهشی مواد نوین، شـماره 7،

 .1391 بهار، 10-1 ص

10- M.J. Attalla, and D.J. Inman, "On

Model Updating Using Neural

Networks", Journal of Mechanical

Systems and Signal Processing, Vol.12,

No.1, pp.135-161, 1998.

11- Mold and Core Test Handbook, 6th

Edition, American Foundry Society

(AFS), 2012.

12- C. Saikaew, and S. Wiengwiset,

”Optimization of Molding Sand

Composition for Quality Improvement

of Iron Castings”, Applied Clay

Science, Volumes 67–68, pp. 26-31,

2012.

13 -م. ح. فتحی ، " مواد قالبگیری برای ریختـه گـری

فلـــــزات "، نـــــشر ارکـــــان ، تهـــــران، 1386.