ABSTRACT:

Twenty-two rock samples having different rock properties are subjected to a set of experimental program in the laboratories of Mining Engineering Department, Istanbul Technical University. In the first stage, rock samples are subjected to a large program of rock mechanics tests. In the second stage, rock samples having size of 40 x 40 x 50 cm are subjected to full-scale rock cutting tests with a type of conical cutter using linear rock cutting machine (LCM) developed under NATO-TU Research program. Some predictor equations using regression analysis are developed to estimate the performance of mechanical excavators utilizing point attack tools. Artificial neural network (ANN) analyses are also performed to see whether it is possible to predict the performance of conical cutters more accurately than statistical analysis. The results indicate that ANN method yields more reliable predictor equations for cutting performance. The ANN models explained in this study will be further refined in future.

INTRODUCTION

The application of roadheaders in both civil and mining engineering fields has increased significantly in recent years and the prediction of cutter forces has emerged as a necessity to provide basic data for machine selection, design and performance prediction for a given rock formation [1-7].

A number of researchers have studied the theoretical aspects of coal and rock cutting process for the last 40 years. However, the most comprehensive and accepted theories are those of Evans? [8-11] for chisel picks and conical picks and of Nishimatsu?s [12] for chisel picks. Although these theories led to a better understanding of the coal and rock cutting process, the prediction of cutting tool performance have been always felt a necessity, since in some cases theoretical estimations of cutter forces were not in good agreement with actual cutting forces due to the complex petrographical, mineralogical and anisotropic nature of rock formations.

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