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Browse AllAircraft assembly systems, as a critical phase in aerospace manufacturing, face significant challenges in maintaining production efficiency and ensuring product quality. This complex manufacturing system exhibits two distinct characteristics: (1) tightly coupled interactions among manufacturing elements involving process sequences, material flows, and equipment utilization; and (2) dynamic resource allocation and material distribution plans. The inherent variability in production element configurations often leads to operational instability and schedule deviations, which may result in abnormal production states. To address these challenges, this study proposes a data-driven predictive framework that integrates Long Short-Term Memory (LSTM) neural networks with multi-criteria evaluation. The developed LSTM-based model effectively forecasts two critical production indicators of cycle time and balance rate, achieving temporal prediction through historical operational data analysis. The proposed methodology facilitates timely anomaly detection and early warning, allowing proactive risk mitigation and ensuring sustained production system stability. This research contributes to advancing intelligent monitoring and control strategies for aircraft assembly operations within data-driven manufacturing environments.
To facilitate the development and application of bulb-flat titanium alloys in aerospace and automotive industries, this study selects TC4 as the research material and employs finite element simulation software to simulate the hot rolling process of TC4 bulb flat titanium. The temperature field, strain field, and metal flow velocity in each rolling pass are analyzed, and rolling experiments are conducted after optimizing the roll pass system. The results indicate that during the rolling process of TC4 bulb flat titanium, the head undergoes relatively smaller deformation, resulting in a slower temperature decrease, whereas the waist experiences greater deformation and a faster temperature drop. A significant temperature difference exists between the core and surface, which can be mitigated by appropriately increasing the roll temperature to reduce heat transfer. Prior to the K4 pass, the billet temperature drops to a level that may affect rolling performance, necessitating furnace reheating. Strain increases progressively with each rolling pass, with higher values observed at the waist compared to the head. A gradual strain transition occurs at the interface between the head and waist. Furthermore, the irregular design of the roll pass leads to a considerable difference in metal flow velocity between the upper and lower surfaces. During the K1 pass rolling, this imbalance can cause the guide guard to be displaced upward and result in roll wrapping. Without altering the roll diameter, shifting the entire roll pass system toward the side with higher metal flow velocity effectively reduces the linear velocity and prevents these issues, ensuring stable billet rolling. Rolling experiments successfully produced the final TC4 bulb flat titanium, thereby validating the feasibility of the optimized roll pass system and the rationality of the selected rolling parameters. It provides the possibility for its development and application in fields such as aircraft and automobiles.
Metallurgical cranes have a high risk of structural fatigue damage and failure under complex working conditions such as high temperature, heavy load, and strong electromagnetic interference. This article proposes a data-driven structural fatigue damage health monitoring system. This system integrates fiber Bragg grating sensing technology, rigid flexible coupling multi-body dynamics simulation, and big data analysis methods to construct a sensor optimization layout strategy based on rigid flexible coupling virtual prototype simulation, achieving real-time perception of stress states in key parts such as the mid span and end beam corners of the main beam. Develop a visualization system that integrates health monitoring, damage diagnosis, and life prediction. This system can dynamically evaluate the structural health status of metallurgical cranes and predict the remaining life of the structure based on a nonlinear cumulative damage model. On site engineering applications have shown that the monitoring and prediction visualization system can effectively improve the intelligent and safe operation and maintenance level of metallurgical cranes, providing a data foundation and possibility for their predictive maintenance.
High-speed wet clutches may experience dynamic instability between the friction plates, leading to rattling vibrations and a significant increase in drag torque. This study employs a homogeneous flow model to characterize the gas-liquid two-phase flow within a high-speed clutch. It establishes a dynamic model for the angular oscillation of friction plates. Finite-element numerical simulations and stability analyses were conducted. The results indicate that as the clutch speed difference increases, the density and viscosity of the two-phase flow decrease rapidly, leading to a sharp reduction in fluid stiffness and damping. Consequently, the friction plates become more susceptible to angular oscillation. The stability of angular oscillation is determined by two key parameters: dimensionless comprehensive stiffness and critical frequency ratio. Higher dimensionless comprehensive stiffness and a lower critical frequency ratio enhance oscillation stability. Numerical evaluations of various groove types reveal that as rotational speed and friction plate clearance increase, the fluid stiffness coefficient, damping coefficient, dimensionless comprehensive stiffness, and critical moment of inertia all decrease, thereby reducing angular oscillation stability. Among the tested groove geometries, enclosed grooves and spiral grooves exhibit superior stability due to their strong hydrodynamic effects, yielding the highest dimensionless comprehensive stiffness. The critical frequency ratio for the self-excited angular oscillation of friction plates is approximately 0.5, termed the half-frequency oscillation characteristic. Experimental data validate the proposed angular oscillation model and its frequency response, providing a theoretical foundation for performance prediction and stability optimization in high-speed clutch design.