Machines

Rotary rock drilling generates vibration signals that capture the dynamic behavior of the drilling system, the interaction between the tool and the rock, and the progression of tool wear. These signals, traditionally considered undesirable, have become a key source of information for condition monitoring and predictive maintenance. This study experimentally investigates vibration sources and diagnostic indicators using a laboratory horizontal drilling stand equipped with accelerometers and controlled operating regimes. Six regimes were evaluated, ranging from idle operation of individual aggregates (motor, pump, hydrogenerator) to drilling of concrete and granite under defined process parameters. Vibration data were analyzed in the time, frequency, and time–frequency domains using RMS, variance, spectral centroid, spectral entropy, FFT-based spectra, autocorrelation, cross-correlation, and spectrograms. The results confirm the research hypothesis that selected vibration-based indicators correlate with tool degradation. Increased RMS values, higher variance, reduced correlation symmetry, and a shift of spectral energy above 6 kHz reliably reflect wear progression and changes in the dynamic response of the system. Spectrograms further reveal transient phases and redistribution of vibration energy during drilling. The findings demonstrate that vibration measurements enable the identification and separation of vibration sources related to aggregates and processes. The extracted diagnostic features form a basis for intelligent monitoring and predictive algorithms in rotary drilling, supporting advanced condition monitoring strategies within Industry 4.0.

The trajectory accuracy of equipment with complex motion paths presents a critical engineering challenge. Targeting the precision issues in the operating trajectory of a carbon-free car, this paper proposes an optimization method for complex mechanical trajectories. Firstly, this study investigates gear backlash-induced return error on the steering precision of a carbon-free cam mechanism of cars. Secondly, considering the cumulative return error of gear transmission between gear groups, a comprehensive mathematical model was established to guide the optimization of cam structure. Finally, the steering accuracy before and after optimization is quantitatively evaluated by trajectory calculation. In addition, the optimized structure was tested and compared with the numerical calculation. The experimental and numerical calculation results are highly consistent. The numerical calculation results show that by adjusting the transmission ratio of the gear set and optimizing the cam profile, the cam deflection angle error is reduced by 24.74% and 27.15%, respectively, and the comprehensive cumulative deflection error of the car is significantly reduced by 45.31%. More importantly, the research provides crucial technical support and guidance for achieving precise control and planning complex paths in automated production lines.

Labor shortages and reliance on manual seedling transplanting constrain pepper production from meeting market demand. To address this mechanization gap, the development of new agricultural machinery is an urgent priority. This study presented kinematic modeling and field validation of an automatic link-driven hopper planting unit for a 3.4 kW walking-type pepper transplanter under development. Kinematic behavior of the hopper was analyzed through mathematical modeling and dynamic simulation and validated under actual transplanting conditions under ridge-patterned field. The optimal design (crank length: 75 mm; 60 rpm) achieved a stable elliptical trajectory that enabled synchronized seedling pickup, tray release, and soil deposition while maintaining vertical alignment. Under this setup, the hopper followed a stable elliptical trajectory (166.88 mm × 318.81 mm), with supply and deposition coordinates of approximately (321 mm, −322 mm) and (293 mm, −617 mm), and peak velocities and accelerations within 0.47 m/s and 1.68 m/s², respectively. Field results showed that the proposed mechanism enabled reliable transplanting performance, achieving a mean planting depth of 27.06 ± 8.18 mm and an uprightness angle of 80.03 ± 7.56°, which fall within agronomic requirements for early pepper establishment. The overall defect rate was low (7.17 ± 3.73%), leading to a 92.83 ± 3.73% success rate at a throughput of 24 seedlings min⁻¹. Variety-dependent responses were observed: Kaltan seedlings exhibited lower defect rates and greater stability than Shinhung seedlings, highlighting the importance of plug strength and stem rigidity in automated systems. These results demonstrate that the mechanism supports fully automated transplanting with acceptable agronomic quality and provides practical design guidance for advancing mechanized pepper production.