DETERMINING THE COMPRESSIVE STRENGTH OF GREEN SAND MOULDS WITH DIFFERENT MOISTURE CONTENTS, USING ARTIFICIAL NEURAL NETWORKS

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

MESHKABADI R.; MARAMI GH.R.; JAHANI K.

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: JOURNAL OF NEW MATERIALS Year:2013 | Volume:4 | Issue:1 (13) Page(s): 47-56

Download Full-Text

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Verion

View:

2,196

Download:

Cites:

Information Journal Paper

Title

DETERMINING THE COMPRESSIVE STRENGTH OF GREEN SAND MOULDS WITH DIFFERENT MOISTURE CONTENTS, USING ARTIFICIAL NEURAL NETWORKS

Author(s)

MESHKABADI R. | MARAMI GH.R. | JAHANI K. | Issue Writer Certificate

Keywords

GREEN SAND MOULDQ3

ARTIFICIAL NEURAL NETWORKSQ3

GREEN SAND COMPRESSIVE STRENGTHQ2

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.

Cites

No record.

References

INVESTIGATION TO THE EFFECT OF PROCESSING PARAMETERS ON IMPACT RESISTANCE OF PIPE-LINE MICRO ALLOYED STEELS USING NEURAL NETWORK

Cite

APA: Copy

MESHKABADI, R., MARAMI, GH.R., & JAHANI, K.. (2013). DETERMINING THE COMPRESSIVE STRENGTH OF GREEN SAND MOULDS WITH DIFFERENT MOISTURE CONTENTS, USING ARTIFICIAL NEURAL NETWORKS. JOURNAL OF NEW MATERIALS, 4(1 (13)), 47-56. SID. https://sid.ir/paper/169737/en

Vancouver: Copy

MESHKABADI R., MARAMI GH.R., JAHANI K.. DETERMINING THE COMPRESSIVE STRENGTH OF GREEN SAND MOULDS WITH DIFFERENT MOISTURE CONTENTS, USING ARTIFICIAL NEURAL NETWORKS. JOURNAL OF NEW MATERIALS[Internet]. 2013;4(1 (13)):47-56. Available from: https://sid.ir/paper/169737/en

IEEE: Copy

R. MESHKABADI, GH.R. MARAMI, and K. JAHANI, “DETERMINING THE COMPRESSIVE STRENGTH OF GREEN SAND MOULDS WITH DIFFERENT MOISTURE CONTENTS, USING ARTIFICIAL NEURAL NETWORKS,” JOURNAL OF NEW MATERIALS, vol. 4, no. 1 (13), pp. 47–56, 2013, [Online]. Available: https://sid.ir/paper/169737/en

Related Journal Papers