Variable inlet guide vane (VIGV) control is among the most energy-efficient flow regulation methods for axial pumps because it adjusts the inlet swirl angle directly while preserving high hydraulic efficiency. However, unlike rotational-speed control, whose performance curves scale straightforwardly through the affinity laws, VIGV control alters the intrinsic shape of the head-flow (H-Q) curve at each vane angle and therefore requires angle-specific prediction. This study proposes a transition-informed Gaussian process regression (TI-GPR) model that augments standard GPR by explicitly incorporating the gradient sign-reversal point of the S-shaped characteristic curve through adaptive region splitting and sigmoid-based blending. A four-factor evaluation covering CV strategy, training-data sparsity, input dimensionality, and model type shows that, even when only Q–H data are available (2D input), TI-GPR lowers the relative MAPE by 17.525% under sparse interpolation and by 35.938% under extrapolation relative to the baseline GPR model. Adding valve-position information (3D input) improves the accuracy of both models further, and TI-GPR retains its advantage. These results demonstrate that a minimal structural modification embedding the stability-gradient transition boundary can yield substantial predictive gains, particularly in data-scarce regimes.

Magnetic gears transmit torque via non-contact electro-magnetic coupling, which eliminates mechanical contact and significantly reduces wear, backlash, and noise compared to traditional mechanical gears. These benefits make magnetic gears particularly appealing for high-precision, high-reliability applications. However, achieving both high torque density and high gear ratios necessitates an optimized structural design that promotes efficient magnetic flux distribution while minimizing leakage and saturation. This study focuses on a hollow-type magnetic gear for collaborative robots that offers a high gear ratio. It employs topology optimization in conjunction with finite element analysis (FEA) to enhance torque density and efficiency. Key design variables, such as the geometry of the ferromagnetic core and the arrangement of permanent magnets, were optimized to increase average torque and reduce torque ripple and electro-magnetic losses. A prototype based on the optimized model was fabricated, and its performance was validated using a conventional direct torque measurement system. Experimental results were compared with simulation predictions to evaluate accuracy and analyze loss characteristics. The findings demonstrate the effectiveness of the proposed optimization approach and provide practical guidelines for designing high-efficiency magnetic gears suitable for advanced drive systems, including electric mobility and renewable energy applications.

The aim of this paper is to investigate the improvement of surface characteristics of Stellite21 deposited layer by powder feeding type of direct energy deposition (DED) process using a plasma electron beam. Re-melting experiments of the deposited specimen is performed using a three-dimensional finishing system with a plasma electron beam. The acceleration voltage and the travel speed of the electron beam are chosen as process parameters. The effects of the process parameters on the surface roughness and the hardness of the re-melted region are examined. The formation of the re-melted region is observed using an optical microscope. Results of these experiments revealed that the re-melting process using a plasma electron beam can greatly improve the surface qualities of the Stellite21 deposited layer by the DED process.