Mechanism and Field Validation of Lithological Interface Identification via Transient Drilling Responses on a Digital Drilling Platform

The monitoring technology used during drilling has become a crucial means of gathering information about the underground rock mass. However, the drilling response parameters are affected by the coupling of operating parameters and rock mass properties, which leads to the challenge of lithology inversion based on drilling parameters in complex strata. At present, the precise quantitative response mechanism between operating parameters and drilling parameters is still not clear in the common lithology of mining, which restricts the further improvement of the accuracy of lithology identification while drilling and the optimization of drilling technology. In order to improve the measurement of drilling technology, the relationship between rock parameters and drilling parameters in the process of mining drilling is explored. This paper carried out physical and mechanical experiments; built a small drilling platform (including magnetic suction drilling, a data monitoring system, and a rock confining pressure system); carried out three different specifications, 330 r/min, 360 r/min, and 390 r/min, of the initial speed of the drilling experiment; and added 330 r/min initial-speed-drilling different-strength rock-drilling experiments. The experimental results show that rock drilling is divided into three stages: the initial stage of drilling, the crack propagation stage, and the bit retreating stage. The rotation speed has a great influence on the drilling speed, torque, weight on bit, and drilling time. According to the Pearson fitting relationship of drilling parameters, the correlation between F and PR is −0.783, indicating a strong positive correlation, and the correlation between RPM and PR is 0.827, indicating a strong negative correlation. The power function y = axb is used to fit the drilling parameters and rock parameters. The fitting effect is good, and the torque and uniaxial tensile strength R2 is as high as 0.9966. The experimental conclusion provides a theoretical basis for lithology identification in intelligent mining drilling and discusses the feasibility of a dynamic monitoring scheme for the drilling rig.

This paper reports a novel oscillation control algorithm for MEMS vibratory gyroscopes using a modified electromechanical amplitude modulation (MEAM) technique, which enhances the robustness against the frequency variation of the driving mode, compared to the conventional EAM (CEAM) scheme. In this approach, the carrier voltage exerted on the proof mass is frequency-modulated by the drive resonant frequency. Accordingly, the pick-up signal from the interface circuit involves a constant-frequency component that contains the amplitude and phase information of the vibration displacement. In other words, this informational detection signal is independent of the mechanical resonant frequency, which varies due to different batches, imprecise micro-fabrication and changing environmental temperature. In this paper, the automatic gain control loop together with the phase-locked loop are simultaneously analyzed using the averaging method and Routh–Hurwitz criterion, deriving the stability condition and the parameter optimization rules of the transient response. Then, a simulation model based on the real system is set up to evaluate the control algorithm. Further, the proposed MEAM method is tested using a field-programmable-gate-array based digital platform on a capacitive vibratory gyroscope. By optimizing the control parameters, the transient response of the drive amplitude reveals a settling time of 45.2 ms without overshoot, according well with the theoretical prediction and simulation results. The first measurement results show that the amplitude variance of the drive displacement is 12 ppm in an hour while the phase standard deviation is as low as 0.0004°. The mode-split gyroscope operating under atmospheric pressure demonstrates an outstanding performance. By virtue of the proposed MEAM method, the bias instability and angle random walk are measured to be 0.9° h−1 (improved by 2.4 times compared to the CEAM method) and 0.068° (√h)−1 (improved by 1.4 times), respectively.

High power switch-mode rectifiers offer advantages over conventionally used line-commutated thyristor rectifiers in terms of size and cost. This paper describes operation of the high power switch-mode rectifier based on an original control strategy which is digitally implemented. The rectifier is operated in a constant output current mode. The control strategy provides a fast transient response to disturbances and achieves equal current sharing between the buck converter modules which are paralleled within the switch-mode rectifier. Equal current sharing is maintained in steady-state as well as in transient operation. The paper demonstrates that the transient response to load disturbances achievable with the proposed control strategy is superior to the response obtained with the conventional control strategy in terms of the output current overshoot and the settling time. The control algorithm is digitally implemented on a DSP/FPGA based digital control platform and its performance is verified on a 2 kW scaled-down experimental setup of the switch-mode rectifier.

The livestock sector plays a crucial role in food security and rural economic resilience; however, goat farming management in developing economies remains largely traditional and weakly integrated with structured environmental accounting systems. This study develops and validates a Material Flow Cost Accounting (MFCA)-Driven Smart Goat Livestock Management System, which integrates environmental management accounting, Internet of Things (IoT) monitoring, emission estimation, and artificial intelligence (AI)-based decision support within a unified digital platform. Using a design science research approach combined with field validation, the system was implemented in a medium-scale goat farm over a two-month period. The MFCA model quantified material inputs and outputs in both physical and monetary terms, including feed conversion, waste generation, and methane (CH₄) and nitrous oxide (N₂O) emissions based on IPCC Tier 1 guidelines. The results demonstrate improvements in feed efficiency (from 74% to 84%), mortality reduction (from 8% to 4%), increased data accuracy (from 60% to 92%), and a 22% improvement in eco-efficiency ratios. The AI module achieved 87% accuracy in estrus detection and 84% accuracy in early disease classification. The study extends MFCA application from manufacturing to biological production systems and introduces the concept of accounting-driven smart farming, where environmental accounting is embedded within digital infrastructure. The findings contribute to the advancement of Digital Environmental Management Accounting (Digital EMA) and provide a scalable model for sustainable livestock transformation in emerging economies.