A study of rock cutting with point attack picks

Abstract

Hard rock mining is currently carried out by drill-and-blast. With the increasing demand for automated, selective and remote mining, the industry hopes to replace conventional drill-and-blast with mechanical excavation. Modern mining machines have sufficient power for cutting hard rocks.The 'bottleneck' which limits the use of mechanical excavation for hard rock mining is the cutting tool wear. Rock cutting tools usually use tungsten carbide (WC) tipped picks. The WC tipped picks are effective and adequate for cutting relatively soft rocks, but unsuitable for hard rocks. To address this issue, CSIRO has developed Super Material Abrasive Resistant Tool (SMART*CUT), aiming at providing an effective cutting tool for hard rock mining. The key feature of the SMART*CUT technology is replacing the conventional WC with thermally stable diamond composite (TSDC) as the cutting tip of the pick. The main advantages of the TSDC tipped picks include (a) good thermal stability, (b) high wear resistance and (c) the ability to mine relatively hard deposits in comparison with the WC tipped picks. The disadvantage of the TSDC tipped picks is their low fracture toughness. To optimise the application of the TSDC tipped picks, this work aimed to improve the understanding of the rock cutting process and mechanisms, in particular to gain a better insight into the interaction between tools and rocks through systematic experimental and numerical studies. The rock cutting processes with the TSDC and WC tipped point attack picks were comprehensively investigated in terms of cutting force, specific energy, tool tip temperature and cutting chips.A study of rock cutting was systematically carried out using the Taguchi method to determine the effects of the cutting parameters on the performance of the TSDC tipped picks. The signal-to-noise (S/N) ratios and the analysis of variance (ANOVA) were applied to investigate the effects of the depth of cut, attack angle, spacing and cutting speed on the mean cutting, normal and bending forces involved in the rock cutting process. The statistical significance of process factors was determined and the optimum parametric combinations were successfully identified. Furthermore, the multiple linear regression (MLR) and artificial neural network (ANN) techniques were adopted to develop the empirical models for predicting the mean cutting and normal forces with the TSDC tipped picks. The established empirical force models showed good predictive capabilities with acceptable accuracy. The ANN models offered better accuracy and less deviation than the MLR models.The pick cutting temperature involved in rock cutting was measured using thermal infrared imaging and embedded thermocouple techniques. The temperature distribution in the cutting area was acquired by using a thermal infrared camera with high speed imaging rates. The results showed that II the TSDC tipped picks generated much lower thermal energy and much fewer sparks than the WC tipped picks. The temperature at the pick tip in the contact area wi...