Extracting periodic fault pulses caused by gear surface fatigue in reducers is often hindered by transmission path interference and strong background noise. Moreover, the traditional Variational Mode Decomposition (VMD) and Maximum Correlation Kurtosis Decomposition (MCKD) method rely on manual parameter selection, which limits its practicality. To address these issues, this paper proposes a parameter-adaptive VMD-MCKD method based on vibration signals for extracting gear surface fatigue fault features. Using the reciprocal of the peak indicator squared of decomposed signals as fitness functions, the method employs the global search capability of the Sparrow Search Algorithm to adaptively select optimal VMD-MCKD configurations. The optimized VMD-MCKD method is applied to decompose gear surface fatigue fault signals, effectively filtering out noise while highlighting periodic fault pulses caused by gear fatigue. Envelope demodulation is then performed to extract characteristic frequency components of gear surface fatigue faults. Experimental results demonstrate that the proposed method can adaptively extract periodic fault pulse components from strong noise environments, achieving a 2-fold improvement in signal kurtosis and enhanced robustness.

A 2014Al matrix composite reinforced with 0.8 wt.% graphene nanoplatelets (GNPs) was prepared by pre-dispersion and ultrasonic melt casting. Subsequently, the as-cast 2014Al-GNP composite was subjected to hot extrusion under different parameters, followed by a comparative analysis of the microstructure and properties of the various alloys. Microstructure and phase composition of the prepared samples were characterized using OM, SEM, EDS, EBSD and TEM inspections. The results indicate that the addition of GNPs effectively promoted the refinement of the as-cast matrix alloy microstructure, while hot extrusion with appropriate parameters further refined the microstructure of the as-cast matrix alloy. At an extrusion ratio of 16, the Al₂Cu, Al₂CuMg, and GNPs in the microstructure displayed a band-like distribution along the extrusion direction, with reduced size and enhanced uniformity. Concurrently, the dislocation density and Kernel Average Misorientation (KAM) values of the composite increased significantly, dynamic recrystallization intensified, and the texture was further enhanced. The tensile strength reached 572.1 MPa, hardness was 369.6 HV, and elongation was 11.9%, representing improvements of 89.0%, 92.0%, and 142.9%, respectively, compared to the as-cast matrix alloy. Fracture surface analysis exhibited brittle fracture characteristics in the matrix alloy, while the extruded composite with optimal parameters displayed distinct ductile fracture features. In the extruded aluminum matrix composite, the interface between GNPs and the matrix was clean, with mutual diffusion of Al and C atoms, achieving an excellent interfacial bonding state. The significant enhancement in mechanical properties of the extruded alloy was primarily attributed to grain refinement strengthening, dislocation strengthening, and load transfer strengthening by GNPs.