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Aircraft 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.
With the increasing demand for material microimaging analysis, there is a growing need for advanced precision grinding and polishing equipment, especially for metals, ceramics, and composites. Existing automated systems struggle with handling complex material challenges. This paper presents a fully automated adaptive grinding and polishing machine based on an STM32 microcontroller that handles multi-material samples. The system includes modules for sample access, cleaning, pad replacement, human-computer interaction, and equipment communication. The STM32 microcontroller executes grinding and polishing tasks based on instructions from the host computer while dynamically adjusting PID control parameters using an improved weighted average optimization algorithm. This approach enhances control accuracy, stability, and overall surface treatment quality compared to traditional PID control methods.
This research demonstrates a facile method for fabricating an anti-icing coating through spray deposition on a metallic substrate. A dual-layer structure was designed to enhance icephobic properties: a primer layer incorporating fluorocarbon resin, butyl acetate, and rod-shaped micrometer-sized metal oxides to establish a secondary roughness morphology, followed by a topcoat composed of butyl acetate and nano-scaled superhydrophobic particles. Evaluation of the coating performance revealed a maximum water contact angle of 171.9°, indicating exceptional hydrophobicity. Furthermore, the coating exhibited notable abrasion resistance and anti-icing capabilities against overlaying ice.
Additive manufacturing (AM) processes facilitate the production of components with high geometrical complexity, presenting substantial opportunities for innovation in demanding sectors such as aerospace and biomedical engineering. A significant challenge impeding their broader application is the characteristic surface roughness of as-fabricated parts, which results from the layer-wise construction and the presence of partially melted powder particles. While electrochemical polishing (EP) represents a viable post-processing technique for achieving a smooth surface finish, a comprehensive understanding of how the non-equilibrium microstructures characteristic of AM materials interact with the EP process remains incomplete. This investigation centers on the electrochemical polishing behavior of Ti-6Al-4V alloy fabricated by direct energy deposition (DED), utilizing a sodium chloride-ethylene glycol electrolyte. The findings reveal that the material's distinct engenders anisotropic anodic dissolution. This behavior is attributed to the differential electrochemical potentials among the constituent phases and their crystallographic orientations, which consequently narrows the operational process window for effective, uniform polishing. This preferential dissolution of certain phases results in the formation of a subtle, micro-scale topographical variation that mirrors the orientation of the original columnar grain structure. Notwithstanding this microstructural influence, the EP treatment proved highly successful in refining the surface finish, substantially decreasing the average surface roughness from 0.350 μm to 0.042 μm. Concurrently, the treatment led to a significant enhancement in the alloy's corrosion resistance, attributed to an oxide layer. These findings underscore the critical necessity of accounting for microstructural characteristics when developing optimized electrochemical polishing protocols for additively manufactured components.
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