A Comparative Analysis of Cognitive Systems for the Prediction of Drillability of Rocks and Wear Factor |
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| Authors: | T N Singh A R Gupta R Sain |
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| Institution: | (1) Department of Earth Sciences, Institute for Technology, Powai, Bombay, Mumbai – 76, 400 056, India;(2) Institute of Technology, BHU, Varanasi, 05, India |
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| Abstract: | This paper demonstrates the applicability of cognitive systems or neural networks in predicting the drillibality of rocks
and wear factor using engineering properties of rocks. Drillability of rocks is a useful guide for evaluating the suitability
of drills for different ground operations. The wear factor of different materials subsequently helps in the selection of proper
drills for different drilling operations. Different rocks were tested for Protodyakonov index, impact strength index, shore
hardness number, Schmidt hammer number, drillability and micro bit chisels for wear factor. The data obtained from the tests
were used to train and test the neural network. Results from the analysis demonstrate that cognitive systems are an effective
tool in the prediction and suitability of drilling operations. Application of these predictive models can be a useful tool
to obtain the value of these important parameters, they can save time and help to avoid the tedious process of instrumentation. |
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| Keywords: | cognitive systems rock drillabilty wear factor resilient back propagation Levenberg– Marquardt back propagation one step secant back propagation |
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