To achieve cost-effective micro-/nano-displacement adjustment, this paper integrates the advantages of flexible hinge micro-/nano-displacement transmission. A linear stepper motor is utilized as the driving component to design and develop a high-precision, low-cost micro-/nano-positioning system. The structure, design, and working principles of the micro-/nano-positioning platform are introduced. The scaling factor model between micro-positioning platforms and nano-positioning platforms is analyzed. Static and dynamic models of flexible mechanisms have been established. The dimensions of the mechanical structure and the selection of motors are determined. The mechanical characteristics of the micro-/nano-positioning platform are validated through finite element analysis. To address the characteristic of increasing loads during the transmission process, an intelligent control system based on current feedback is designed and developed. The integration of drive and control provides a high level of system integration. Finally, experimental calibration was conducted to test the motion characteristics of the linear stepper motor-driven micro-/nano-positioning platform. It achieved a minimum displacement control resolution of 100 nm and demonstrated a certain level of stability.

WC-10Co-4Cr coating is highly valued for its corrosion resistance and wear resistance when applied using the high-velocity oxy-fuel (HVOF) spraying method. However, conventional grinding (CG) of this coating presents challenges, including substantial grinding forces and elevated surface temperatures. To address these concerns, our study proposed the utilization of ultrasonic vibration-assisted grinding (UVAG) as a means to enhance the machining properties of HVOF-sprayed WC-10Co-4Cr coatings. Comparative experiments were conducted to analyze the impacts of various factors on the grinding forces and surface roughness in UVAG and CG processes. Additionally, the topography of the ground surfaces was examined to gain insights into the material removal mechanism in UVAG. The experimental outcomes reveal significant reductions in tangential and normal grinding forces, amounting to 15.47% and 22.23%, respectively, in UVAG when compared with CG. Furthermore, UVAG led to a roughly 29.14% decrease in ground surface roughness compared with CG. Microscopic analysis of the ground surfaces using scanning electron microscopy (SEM) indicated that ductile removal was the predominant material removal mode in UVAG. Overall, UVAG was found to be effective in diminishing grinding forces, improving ground surface roughness, and enhancing surface integrity when contrasted with CG. These findings introduce a novel approach for processing WC-10Co-4Cr coatings.

Introduction: Wheat stem rust (Puccinia graminis f. sp. tritici) remains a major threat to wheat production worldwide. Detecting the disease at the pre-symptomatic stage is important for earlier warning and more timely management. Methods: We evaluated hyperspectral imaging and deep learning for pre-symptomatic wheat stem rust detection using a time-series dataset collected at 4–9 days post inoculation (DPI 4–9). Seven representative deep learning models were compared across DPI stages. A weighted cross-entropy strategy was then applied to the three strongest models, and model interpretability was examined using input gradient analysis, SHAP attribution, and vegetation-index screening. Results: The weighted optimization increased overall F1-scores by 10.0%–18.4%. At the pre-symptomatic stage, the best model achieved an F1-score of 0.94 at DPI 4 and 0.99 at DPI 5, enabling detection before visible symptom development at DPI 6–7. Across the interpretability analyses, the 480–550 nm blue–green region emerged as the main source of information for pre-symptomatic detection, whereas the 750–870 nm near-infrared region contributed more general information on disease presence. Discussion: These results show that hyperspectral imaging paired with deep learning can support accurate pre-symptomatic detection of wheat stem rust under controlled experimental conditions and provide useful evidence for future field-scale studies of early disease warning.