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Neural Computing and Applications

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22.08.2016 | Original Article

Performance prediction of circular saw machine using imperialist competitive algorithm and fuzzy clustering technique

verfasst von: Reza Mikaeil, Sina Shaffiee Haghshenas, Sami Shaffiee Haghshenas, Mohammad Ataei

Erschienen in: Neural Computing and Applications | Ausgabe 6/2018

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Abstract

The purpose of this study is the application of meta-heuristic algorithms and fuzzy logic in the optimization and clustering to predict the sawability of dimension stone. Survey and classification of dimension stones based on their physical and mechanical properties can be so impressive in the optimization of machine applications that are in this industry such as circular diamond saw block cutting machine. In this paper, physical and mechanical properties were obtained from laboratory testing on dimension stone block samples collected from 12 quarries located in Iran and their results were optimized and classified by one of the strongest meta-heuristic algorithms and fuzzy clustering technique. The clustering of dimension stone was determined by Lloyd’s algorithm (k-means clustering) based on imperialist competitive algorithm and fuzzy C-mean by MATLAB software. The hourly production rate of each studied dimension stones was considered as a criterion to evaluate the clustering efficacy. The results of this study showed that the Imperialist Competitive algorithm and fuzzy C-mean are very suitable for clustering with respect to the physical and mechanical properties of the dimension stone, and the results obtained showed the superiority of the ICA.

Vorheriger Artikel Prediction and minimization of blast-induced flyrock using gene expression programming and firefly algorithm

Nächster Artikel Finite-time synchronization of stochastic memristor-based delayed neural networks

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Titel: Performance prediction of circular saw machine using imperialist competitive algorithm and fuzzy clustering technique
verfasst von: Reza Mikaeil
Sina Shaffiee Haghshenas
Sami Shaffiee Haghshenas
Mohammad Ataei
Publikationsdatum: 22.08.2016
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 6/2018
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-016-2557-4

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