Faculty Publications
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Item The use of Dimensional Analysis and Optimization of Pneumatic Drilling Operations and Operating Parameters(Springer India sanjiv.goswami@springer.co.in, 2012) Kivade, S.B.; Murthy, C.S.N.; Vardhan, H.Dimensional analysis was used to demonstrate the significance of these important parameters, grouped together in dimensionless numbers which will then allow for optimum use of limited laboratory data to produce better results. It allows for reduction of total effort in designing laboratory experiments, reducing total load and cost, permitting variation of the important dimensional groups rather than individual drilling operating parameters, hence a more efficient design of experiments can be realized. Drilling operations are very expensive endeavors and efforts are continuous by engineers and researchers to achieve the optimum penetration rate. To enhance bit life and penetration rate, optimization of bit design and drilling operations must be realized. To measure the penetration rate of the pneumatic drill, a fabricated pneumatic drill set up available was used. Laboratory tests were carried out to obtain the physical and mechanical properties of the rock samples. Penetration rate has been derived by means of regression statistics method. In order to overcome this drawback, dimensional analysis was used to derive relevant dimensional groups leading to the development of empirical equation of penetration rate. © 2012, The Institution of Engineers (India).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 Laboratory Investigations on Percussive Drilling(Springer India sanjiv.goswami@springer.co.in, 2013) Kivade, S.B.; Murthy, C.S.N.; Vardhan, H.The laboratory investigation was carried out on ten rock samples using pneumatic drill with drill bits of different diameters. In general, the process of drilling always produces sound. Sound is generated from the bit-rock interface regardless of the material of the bit used in drilling. The predicted sound level and penetration rate are a product of the drill power and the physical properties of the rocks penetrated. Rock samples were collected from the field and physical properties of the rocks were determined in the laboratory. The sound level and penetration rates were correlated with the rock properties. The compressive strength and abrasivity exhibit strong correlations with the sound level and penetration rate. It was concluded that, among the rock properties included in this study, the compressive strength and abrasivity values are the dominant ones affecting the penetration rate and sound level of percussive drills. Though ten rock samples have been covered in this study, detailed analysis of only one of them is presented. © 2013, The Institution of Engineers (India).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).