Open AccessSawability ranking of carbonate rock using fuzzy analytical hierarchy process and TOPSIS approaches
Author(s) -
Reza Mikaeil,
Reza Yousefi,
Mohammad Ataei
Publication year - 2011
Publication title -
scientia iranica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.299
H-Index - 51
eISSN - 2345-3605
pISSN - 1026-3098
DOI - 10.1016/j.scient.2011.09.009
Subject(s) - topsis , ideal solution , ranking (information retrieval) , analytic hierarchy process , carbonate , carbonate rock , fuzzy logic , power (physics) , mathematics , computer science , statistics , petroleum engineering , geology , process engineering , engineering , artificial intelligence , operations research , materials science , chemistry , metallurgy , physics , quantum mechanics
The aim of this paper is developing a new hierarchical model to evaluate and rank the sawability (power consumption) of carbonate rock with the use of effective and major criteria, and simultaneously taking subjective judgments of decision makers into consideration. The proposed approach is based on the combination of Fuzzy Analytic Hierarchy Process (FAHP) method with TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) methods. FAHP is used for determining the weights of the criteria by decision makers, and then rankings of carbonate rocks are determined by TOPSIS. The proposed method is applied for Iranian ornamental stone to evaluate the power consumption in rock sawing process. A variety of two groups of carbonate rocks (7 types) were sawn, using a fully-instrumented laboratory cutting rig at different feed rate (200, 300, 400cmmin−1) at constant peripheral speed (1770rpm) and depth of cut (35mm). During the sawing trials, the ampere and power consumption were monitored and calculated as performance characteristics of the saws. The results of the sawing trials (power consumption) were used to verify the result of the applied approach for ranking the carbonate rock sawability. It was concluded that the sawability of carbonate rocks can reliably be ranked, using the developed approach