Search Results (4)

This study investigates the correlations between the wear conditions of conical pick cutters and key variables such as the physical properties (shape, aspect ratio, roughness), explosive potential, health and safety implications, and particle size distribution of coal dust and larger fragments using the linear cutting machine (LCM). This research was conducted within the framework of recent regulatory developments, notably implementing the new silica rule in the mining and construction sectors and climate change consideration. This study reveals critical insights into optimizing operational processes while adhering to stringent health and safety regulations. The findings indicate that as cutting tools wear, there is a significant increase in generated fine particles, including respirable crystalline silica (RCS), which elevates the risk of respiratory diseases and, in the case of coal dust, a higher potential for explosions. The results show that the silica content in respirable dust is a function of rock mineralogy; however, the results showed that the absolute amount of silica-containing dust increased with bit wear in rocks containing pertinent minerals. For the larger fragments, the new bit produced a 1699 fragment count, while the completely worn-out bit produced a 5608 count. The results of the dust concentration show that the new bit produces 89.2 mg/m³ (17.84%); the moderate bit produces 165.1 mg/m³ (33.03%), and the worn-out bit produces 245.6 mg/m³ (49.13%). Moreover, this study highlights the impact of bit wear on the production of larger fragments, which decreases with tool degradation, further contributing to dust generation. These results suggest the necessity for proactive equipment maintenance, enhanced dust control measures, and continuous monitoring of cutting tool wear to ensure compliance with regulatory standards and to protect workers’ health and safety. Full article

This study introduces a simplified method for predicting the optimal cutting conditions to maximize excavation efficiency based on tool forces. A laboratory-scale linear rock-cutting test was conducted using a conical pick on Finike limestone. The tool forces and their ratios were analyzed in relation to cutting parameters such as penetration depth and spacing. While the cutting force (FC) and normal force (FN) increased with the penetration depth and spacing, this relationship could not predict the optimal cutting conditions. The ratio of the mean normal force to the mean cutting force (FN_m/FC_m) increased with the penetration depth and the ratio of spacing to penetration depth (s/d). However, even while including this relationship, predicting optimal cutting conditions remained challenging. The ratio of the peak cutting force to the mean cutting force (FC_p/FC_m) reached a maximum value at a specific s/d, which is similar to the relationship between the specific energy (SE) and s/d. The optimal s/d obtained through the SE methodology was found to be between 3 and 5, and FC_p/FC_m reached a maximum at s/d. The error between the optimal s/d and the s/d in which FC_p/FC_m was maximized was less than 5%. Therefore, it was confirmed that the optimal cutting conditions could be predicted through the relationship between FC_p/FC_m and s/d. Additionally, by using the results from previous studies, the optimal cutting conditions obtained from the SE methodology and the proposed methodology were found to agree within a margin of error of 20%. The proposed methodology can be beneficial for the design of cutter heads and the operation of excavation machines. Full article

In underground coal mining, machine operators put themselves at risk when getting close to the machine or cutting face to observe the process. To improve the safety and efficiency of machine operators, a cutting force sensor is proposed. A linear cutting machine is used to cut two separate coal samples cast in concrete with conical pick cutters to simulate mining with a continuous miner. Linear and neural network regression models are fit using 100 random 70:30 test/train splits. The normal force exceeds 60 kN during the rock-cutting tests, and it is averaged using a low pass filter with a 10 Hertz cutoff frequency. The sensor uses measurements of the resonant frequency of capacitive cells in a steel case to determine cutting forces. When used in the rock-cutting experiments, the sensor conforms to the tooling and the stiffness and sensitivity are increased compared to the initial configuration. The sensor is able to track the normal force on the conical picks with a mean absolute error less than 6 kN and an $R^2$ score greater than 0.60 using linear regression. A small neural network with a second-order polynomial expansion is able to improve this to a mean absolute error of less than 4 kN and an $R^2$ score of around 0.80. Filtering measurements before regression fitting is explored. This type of sensor could allow operators to assess tool wear and material type using objective force measurements while maintaining a greater distance from the cutting interface. Full article

The rapid wear of conical picks used in rock cutting heads in the mining industry has a significant economic impact in cost effectiveness for a given mineral extraction business. Any mining facility could benefit from decreasing the cost along with a substantial durability increase of a conical pick; thus, the hardfacing method of production and regeneration should be taken into account. In order to automatize the regeneration, the wear rate assessment is necessary. This paper presents a methodology used to create a 3D photogrammetric model of most of the commercially available tangential-rotary cutters in their before and after abrasive exploitation state. An experiment of three factors on two levels is carried out to indicate the proper setup of the scanning rig to obtain plausible results. Those factors are: light level, presence of polarizing filter and the distance from the scanned object. The 3D scan of the worn out specimen is compared to the master model via algorithm developed by the authors. This approach provides more detailed information about the wear mechanism and can help either in roadheader cutting head diagnostics or to develop a strategy and optimize the toolpath for the numerically controlled hardfacing machine. Full article