Gear defect detection directly affects the operational reliability of critical equipment in fields such as automotive and aerospace. Gear defect detection technology based on machine vision, leveraging the advantages of non-contact measurement, high efficiency, and cost-effectiveness, has become a key support for quality control in intelligent manufacturing. However, it still faces challenges including difficulties in semantic alignment of multimodal data, the imbalance between real-time detection requirements and computational resources, and poor model generalization in few-shot scenarios. This paper takes the paradigm evolution of gear defect detection technology as the main line, systematically reviews its development from traditional image processing to deep learning, and focuses on the innovative application of intelligent algorithms. A research framework of “technical bottleneck-breakthrough path-application verification” is constructed: for the problem of multimodal fusion, the cross-modal feature alignment mechanism based on Transformer network is deeply analyzed, clarifying its technical path of realizing joint embedding of visual and vibration signals by establishing global correlation mapping; for resource constraints, the performance of lightweight models such as MobileNet and ShuffleNet is quantitatively compared, verifying that these models reduce Parameters by 40–60% while maintaining the mean Average Precision essentially unchanged; for small-sample scenarios, few-shot generation models based on contrastive learning are systematically organized, confirming that their accuracy in the 10-shot scenario can reach 90% of that of fully supervised models, thus enhancing generalization ability. Future research can focus on the collaboration between few-shot generation and physical simulation, edge-cloud dynamic scheduling, defect evolution modeling driven by multiphysics fields, and standardization of explainable artificial intelligence. It aims to construct a gear detection system with autonomous perception capabilities, promoting the development of industrial quality inspection toward high-precision, high-robustness, and low-cost intelligence.

As a commonly used component in robot joints, helical planetary gear system is restricted from further application in the robotics industry due to excessive maximum subsurface shear stress and vibration amplitude during their meshing motion. Tooth modification can effectively reduce the maximum subsurface shear stress and vibration amplitude of gears, making it particularly important to conduct research on the modification of helical planetary gear trains. In this study, a lumped mass method is first adopted to establish a bending-torsion-axial coupling dynamic model of the helical planetary gear train. Subsequently, multi-objective optimization modification research on the left tooth flank of the planetary gear is carried out using both traditional empirical formulas and an improved MOPSO, respectively. Then, the finite element method is employed to analyze the maximum subsurface shear stress of planetary gears under three scenarios: unmodified, traditionally modified, and modified with the improved MOPSO. Finally, the 4th-order Runge-Kutta method is used to solve the bending-torsion-axial coupling dynamic model of the helical planetary gear train system, thereby obtaining the vibration amplitude of the sun gear under the three scenarios. The research results show that the empirical formula method and the improved MOPSO reduce the maximum subsurface shear stress of the planetary gear by 12.629% and 30.107%, respectively, and decrease the vibration amplitude of the sun gear by 10.26% and 19.29%, respectively. This study provides theoretical and data support for the development of helical planetary gear modification and promotes its further application in the robotics industry.

As a commonly used component in robot joints, helical planetary gear system is restricted from further application in the robotics industry due to excessive maximum subsurface shear stress and vibration amplitude during their meshing motion. Tooth modification can effectively reduce the maximum subsurface shear stress and vibration amplitude of gears, making it particularly important to conduct research on the modification of helical planetary gear trains. In this study, a lumped mass method is first adopted to establish a bending to rsionaxial coupling dynamic model of the helical planetary gear train. Subsequently, multi-objective optimization modification research on the left tooth flank of the planetary gear is carried out using both traditional empirical formulas and an improved Multi-Objective Particle Swarm Optimization (MOPSO) algorithm featuring physics-informed search boundaries and an automated optimal selection mechanism, respectively. Then, the finite element method is employed to analyze the maximum subsurface shear stress of planetary gears under three scenarios: unmodified, traditionally modified, and modified with the improved MOPSO. Finally, the 4th-order Runge Kutta method is used to solve the bending to rsionaxial coupling dynamic model of the helical planetary gear train system, thereby obtaining the vibration amplitude of the sun gear under the three scenarios. The research results show that the empirical formula method and the improved MOPSO reduce the maximum subsurface shear stress of the planetary gear by 12.629% and 30.107%, respectively, and decrease the vibration amplitude of the sun gear by 10.26% and 19.29%, respectively. This study provides theoretical and data support for the development of helical planetary gear modification and promotes its further application in the robotics industry.