Please use this identifier to cite or link to this item: https://idr.l1.nitk.ac.in/jspui/handle/123456789/12616
Title: Prediction of penetration rate and sound level produced during percussive drilling using regression and artificial neural network
Authors: Kivade, S.B.
Murthy, C.S.N.
Vardhan, H.
Issue Date: 2012
Citation: International Journal of Earth Sciences and Engineering, 2012, Vol.5, 6, pp.1639-1644
Abstract: 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.
URI: http://idr.nitk.ac.in/jspui/handle/123456789/12616
Appears in Collections:1. Journal Articles

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