Measurement of bit-rock interface temperature and wear rate of the tungsten carbide drill bit during rotary drilling

Abstract

Rock drilling is an essential operation in mining industries. Temperature at the bit-rock interface plays a major role in the wear rate of the drill bit. This paper primarily focuses on the wear rate of tungsten carbide (WC) drill bit and the interrelationship between temperature and wear rate during rotary drilling operations conducted using a computer numerical control (CNC) machine. The interrelationship between the temperature and wear rate was studied with regard to three types of rock samples, i.e., fine-grained sandstone (FG) of uniaxial compressive strength (UCS) that is 17.83 MPa, medium-grained sandstone (MG) of UCS that is 13.70 MPa, and fine-grained sandstone pink (FGP) of UCS that is 51.67 MPa. Wear rate of the drill bit has been measured using controlled parameters, i.e., drill bit diameter (6, 8, 10, 12, and 16 mm), spindle speed (250, 300, 350, 400, and 450 rpm), and penetration rate (2, 4, 6, 8, and 10 mm/min), respectively. Further, a fully instrumented laboratory drilling set-up was utilized. The weight of each bit was measured after the bit reached 30 mm depth in each type of the rock sample. Furthermore, effects of the bit-rock interface temperature and operational parameters on wear rate of the drill bits were examined. The results show that the wear rate of drill bits increased with an increase in temperature for all the bit-rock combinations considered. This is due to the silica content of the rock sample, which leads to an increase in the frictional heat between the bit-rock interfaces. However, in case of medium-grained sandstone, the weight percentage (wt%) of SiO2 is around 7.23 wt%, which presents a very low wear rate coefficient of 6.33×10−2 mg/(N·m). Moreover, the temperature rise during drilling is also minimum, i.e., around 74 °C, in comparison to that of fine-grained sandstone and fine-grained sandstone pink. In addition, this paper develops the relationship between temperature and wear rate characteristics by employing simple linear regression analysis. © 2019, The Author(s).