Prediction of specific cutting energy in natural stone cutting processes using the neuro-fuzzy methodology

Abstract

Specific cutting energy (SEcut) values are used for the determination of energy requirements of the stone cutting process and are thus useful in predicting the cost and production schedule. In this study, adaptive hybrid intelligence (AHI) techniques were employed to develop SEcut prediction models based on 40 different natural building stones in nineteen different stone processing plants. The feed rate, depth of cut, which are cutting process working parameters, and uniaxial compressive strength, bending strength and point load strength of the rock to be cut which constitute rock physico-mechanical properties were used as the input parameters in the development of SEcut prediction models. The AHI techniques included Adaptive Neuro-Fuzzy Inference System (ANFIS), Dynamic Evolving Neuro-Fuzzy Inference System (DENFIS), and Evolving Fuzzy Neural Networks (EFuNN). Among the AHI techniques, ANFIS gave the best SEcut prediction accuracy. The results also showed that it is possible to predict specific cutting energy of natural stone cutting operations with higher accuracy (R2=0.95) with the developed ANFIS prediction models using depth of cut, feed rate and uniaxial compressive strength values of natural building stones.