Rock excavation has had extensive applications in various civil, mining, and oil projects from the past to the present. Therefore, excavation performance eva‎luation can significantly impact the success and productivity of operations. The four functional parameters of excavation, including forces acting on the tool, specific energy, fines production, and tool vibration are among the most critical factors affecting excavation performance and efficiency. In eva‎luating the performance of a cutting machine with a conical tool, it is important to note that if the operational behavior is not measured, other parts of the cutting machine can be subjected to excessive stress. This can lead to the replacement of expensive components and significant excavation stoppages. Therefore, this research focuses on eva‎luating cutting performance parameters and their interaction with the rock properties. For this purpose, natural rock samples from different parts of Iran were collected and, after preparation, cutting operations with conical tools were carried out under unrelieved cutting mode at cutting depths of 0.5 to 6 mm. To examine cutting performance parameters, the force and vibration acting on the cutting tool were recorded by relevant sensors, and sieve analysis was conducted on the fragments obtained from the cutting process. Subsequently, after recording the data, the relationship between rock properties and cutting performance parameters was examined. To achieve this, variations in specific energy relative to cutting depth were first considered, and the threshold cutting depth was determined for each sample. Then, using linear and nonlinear regression analysis, models were developed to eva‎luate cutting performance parameters under effective conditions. The performance eva‎luation results of the models showed that nonlinear models performed better than linear models, with R² values of 0.881, 0.917, and 0.908, respectively. These nonlinear models were capable of estimating the force acting on the cutting tool, specific energy, and fines under effective cutting conditions, considering rock properties. Furthermore, considering the critical cutting depth and employing a hierarchical clustering algorithm, a three-class classification for fines production was developed. Subsequently, using the force and cutting rate parameters and applying a support vector machine algorithm, a two-dimensional space was constructed to determine the transition zone from fines to chips. After constructing the two-dimensional space, the model's performance was assessed using relevant performance eva‎luation criteria. The results indicated that the developed model could predict the transition limit from fines to chips with 91.3% accuracy. Finally, considering the cutting conditions and the transition zone from fines to chips, an updated transition zone was developed to adjust the cutting conditions to enhance cutting performance under effective conditions. In the final stage, using the force and vibration signals recorded at each stage, the frequencies of fines and chip production were examined using a fast Fourier transform. Initially, by preprocessing the signals, noise was removed from each signal. Then, each signal was decomposed using a wavelet transform, and the frequencies of fines and chip production were analyzed.

ابراهيم قاسمي ورنوسفادراني , جعفر خادمي حميدي

جمال رستمي

هادي حسيني , كورش شهريار , شكرالله زارع