Development of Intelligent Systems to Predict Diamond Wire Saw Performance

Assessment of wear rate is an inseparable section of the sawability of dimension stone and an essential task to optimization in diamond wire saw performance. The aim of this research is to provide an accurate, practical and applicable model for predicting the wear rate of diamond bead based on rock properties using applications and performances of intelligent systems. In order to reach this purpose, 38 cutting test results with 38 different rocks were used from andesites, limestones and real marbles quarries located in eleven areas in Turkey. Prediction of wear rate is determined by optimization techniques like Multilayer Perceptron (MLP) and hybrid Genetic algorithm –Artificial neural network (GA-ANN) models that were utilized to build two estimation models by MATLAB software. In this study, 80% of the total samples were used randomly for training dataset and the remaining 20% was considered as testing data for GA-ANN model. Further, accuracy and performance capacity of models established were investigated using root mean square error (RMSE), coefficient of determination (R2) and standard deviation (STD). Finally, a comparison was made among performances of these soft computing techniques for predicting and the results obtained clearly indicated hybrid GA-ANN model with coefficient of determination (R2) of training = 0.95 and testing = 0.991 can get more accurate predicting results in comparison with MLP models.

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