Performance evaluation of chain saw machines for dimensional stones using feasibility of neural network models

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.

MOHAMMADI J.; ATAEI M.; KAKAIE R.; MIKAEIL R.; Shaffiee Haghshenas S.

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

Journal Paper

Paper Information

Journal: JOURNAL OF MINING AND ENVIRONMENTAL (INTERNATIONAL JOURNAL OF MINING & ENVIRONMENTAL ISSUES) Year:2019 | Volume:10 | Issue:4 Page(s): 1105-1119

Download Full-Text

Persian Verion

View:

362

Download:

220

Cites:

Information Journal Paper

Title

Performance evaluation of chain saw machines for dimensional stones using feasibility of neural network models

Author(s)

Keywords

Dimensional StoneQ1

Chain Saw MachineQ1

Production RateQ1

Group Method of Data HandlingQ1

Carbonate RocksQ1

Abstract

Prediction of the Production Rate of the cutting Dimensional Stone process is crucial, especially when Chain Saw Machines are used. The cutting dimensional rock process is generally a complex issue with numerous effective factors including variable and unreliable conditions of the rocks and cutting machines. The Group Method of Data Handling (GMDH) type of neural network and Radial Basis Function (RBF) neural network, as two kinds of the soft computing method, are powerful tools for identifying and assessing the unpredicted and uncertain conditions. Hence, this work aims to develop prediction models for estimating the Production Rate of Chain Saw Machines using the RBF neural network and GMDH type of neural network, and then to compare the results obtained from the developed models based on the performance indices including value account for, root mean square error, and coefficient of determination. For this purpose, the parameters of 98 laboratory tests on 7 Carbonate Rocks are accurately investigated, and the Production Rate of each test is measured. Some operational characteristics of the machines, i. e. arm angle, chain speed, and machine speed, and also the three important physical and mechanical characteristics including uniaxial compressive strength, Los Angeles abrasion test, and Schmidt hammer (Sch) are considered as the input data, and another operational characteristic of the machines, i. e. Production Rate, is considered as the output dataset. The results obtained prove that the developed GMDH model is able to provide highly promising results in order to predict the Production Rate of Chain Saw Machines based on the performance indices.

Cites

No record.

References

No record.

Cite

APA: Copy

MOHAMMADI, J., ATAEI, M., MIKAEIL, R., Shaffiee Haghshenas, S., & KAKAIE, R.. (2019). Performance evaluation of chain saw machines for dimensional stones using feasibility of neural network models. JOURNAL OF MINING AND ENVIRONMENTAL (INTERNATIONAL JOURNAL OF MINING & ENVIRONMENTAL ISSUES), 10(4 ), 1105-1119. SID. https://sid.ir/paper/256325/en

Vancouver: Copy

MOHAMMADI J., ATAEI M., MIKAEIL R., Shaffiee Haghshenas S., KAKAIE R.. Performance evaluation of chain saw machines for dimensional stones using feasibility of neural network models. JOURNAL OF MINING AND ENVIRONMENTAL (INTERNATIONAL JOURNAL OF MINING & ENVIRONMENTAL ISSUES)[Internet]. 2019;10(4 ):1105-1119. Available from: https://sid.ir/paper/256325/en

IEEE: Copy

J. MOHAMMADI, M. ATAEI, R. MIKAEIL, S. Shaffiee Haghshenas, and R. KAKAIE, “Performance evaluation of chain saw machines for dimensional stones using feasibility of neural network models,” JOURNAL OF MINING AND ENVIRONMENTAL (INTERNATIONAL JOURNAL OF MINING & ENVIRONMENTAL ISSUES), vol. 10, no. 4 , pp. 1105–1119, 2019, [Online]. Available: https://sid.ir/paper/256325/en

Related Journal Papers