Faculty Publications
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Item Assessment of noise and effect of thrust on penetration rate in percussive drilling(2011) Kivade, S.B.; Murthy, C.S.N.; Vardhan, H.Rock drills are a major source of noise in the mining industry, with levels reaching as high as 115 dbA at the operator's ear. Noise of this amplitude has long been recognized as a serious health hazard. The two major noise sources in pneumatic rock drills are exhaust air and impact of the piston against the drill rod shank. The exhausted compressed air produces noise because it is released at a relatively high pressure and in bursts or pulses. While the impact of the piston against the shank causes vibrations in the drill rod and in different parts of the drill body which then radiate noise. Mufflers can reduce the noise to the order of 105 dbA, at which level the drill rod vibrations become the dominant noise source. The rock types selected for the present study are basalt, gabro granite, pink granite and lime stone. The range of applied thrust varies between 10 kg to 100 kg. All the holes were drilled for a fixed time of one minute and penetration rates were obtained. It was observed that very low thrust results in low penetration rates but on the other hand, even very high thrusts do not produce high penetration rates at high operating air pressures. Optimum thrusts were obtained for each rock type experimentally. This paper deals in detail effect of applied thrust on the penetration rate and sound level of a conventional percussive drill.Item Prediction of penetration rate and sound level produced during percussive drilling using regression and artificial neural network(2012) Kivade, S.B.; Murthy, C.S.N.; Vardhan, H.The main objective of this investigation is to develop a general prediction model and to study the effect of predictor variables such as uniaxial compressive strength, air pressure and thrust on penetration rate and sound level produced during percussive drilling of rocks. The experiment was carried out using three levels Box-Behnken design with full replication in 15 trials. Modeling was done using artificial neural network (ANN) and multipleregression analysis (MRA). These techniques can be utilized for the prediction of process parameters. Comparison of artificial neural network and multiple linear regression models was made and found that error rate was smaller in ANN than that predicted by MRA in terms of sound level and penetration rate. © 2012 CAFET-INNOVA TECHNICAL SOCIETY.Item ANN Models for Prediction of Sound and Penetration Rate in Percussive Drilling(Springer India sanjiv.goswami@springer.co.in, 2015) Kivade, S.B.; Murthy, C.S.N.; Vardhan, H.In the recent years, new techniques such as; Artificial Neural Network (ANN) were employed for developing of the predictive models to estimate the needed parameters. Soft computing techniques are now being used as alternate statistical tool. In this study, ANN models were developed to predict rock properties of sedimentary rock, by using penetration and sound level produced during percussive drilling. The data generated in the laboratory investigation was utilized for the development of ANN models for predicting rock properties like, uniaxial compressive strength, abrasivity, tensile strength, and Schmidt rebound number using air pressure, thrust, bit diameter, penetration rate and sound level. Further, ANN models were also developed for predicting penetration rate and sound level using air pressure, thrust, bit diameter and rock properties as input parameters. The constructed models were checked using various prediction performance indices. ANN models were more acceptable for predicting rock properties. © 2015, The Institution of Engineers (India).