Experimental Investigation on Estimation and Prediction of Sound in Percussive Drilling

Abstract

This research work was taken up with the objectives of developing general prediction models for the determination of uni-axial compressive strength (UCS), abrasivity, tensile strength (TS) and Schmidt rebound number (SRN) for sedimentary and igneous rocks using penetration rate and sound level produced during percussive drilling. To carry out this investigation fabricated pneumatic drill set-up on the laboratory scale was used. In the present work shale, dolomite, sand stone, lime stone and hematite were the sedimentary rocks, whereas dolerite, soda granite, black granite, basalt and gabbros were the igneous rocks used in this investigation. For all the above mentioned rocks their mechanical properties were determined as per the suggested methods of International Society of Rock Mechanics (ISRM). The laboratory investigation on all the sedimentary and igneous rocks using the drill set-up was carried out to find the penetration rate (mm/s) and sound level (dB (A)) produced by varying air pressure from 392 to 588 kPa, thrust from 100 to 1000 N and with varying drill bits and types (integral chisel drill bit: 30, 34 and 40 mm diameter, threaded (R22) cross drill bit: 35 and 38 mm diameter). The data generated in the laboratory investigation was utilized for the development of regression models for predicting rock properties like, UCS, abrasivity, TS, and SRN using air pressure, thrust, bit diameter, penetration rate and sound level. Further, regression models were also developed for predicting penetration rate and sound level using air pressure, thrust, bit diameter and rock properties as input parameters. In a similar way, i.e. utilizing the same input parameters for determining the rock properties and predicting the sound level and penetration rate, Artificial Neural Network (ANN) models were developed. A comparison was made between the results obtained using various regression models developed and the ANN models. Results of this investigation indicate that ANN models are superior over regression models.