Dynamic simulation whole process optimization and experimental verification of milling aviation aluminum alloy aircraft structure

Mo, Genlin; Liu, Jing; Li, Liangliang; Li, Pengfei; Gong, Yadong; Zhou, Jing; Lu, Jinzhong

doi:10.1007/s00170-022-09734-8

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…As a fast, economical, safe, and efficient research approach, simulation modelling has been used in the aviation industry for various purposes, including aircraft design [44], optimization [45], manufacturing [46], air traffic management [47], pilot training [48], accident investigation [49], and MRO (Maintenance, Repair and Overhaul) [50]. For the fuel system, simulation can assist the system's design, verification, and optimization processes.…”

Section: Fuel System Simulationmentioning

confidence: 99%

Intelligent Fault Diagnosis of an Aircraft Fuel System Using Machine Learning—A Literature Review

King

Jennions

2023

Machines

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The fuel system, which aims to provide sufficient fuel to the engine to maintain thrust and power, is one of the most critical systems in the aircraft. However, possible degradation modes, such as leakage and blockage, can lead to component failure, affect performance, and even cause serious accidents. As an advanced maintenance strategy, Condition Based Maintenance (CBM) can provide effective coverage, by combining state-of-the-art sensors with data acquisition and analysis techniques to guide maintenance before the asset’s degradation becomes serious. Artificial Intelligence (AI), particularly machine learning (ML), has proved effective in supporting CBM, for analyzing data and generating predictions regarding the asset’s health condition, thus influencing maintenance plans. However, from an engineering perspective, the output of ML algorithms, usually in the form of data-driven neural networks, has come into question in practice, as it can be non-intuitive and lacks the ability to provide unambiguous engineering signals to maintainers, making it difficult to trust. Engineers are interested in a deterministic decision-making process and how it is being revealed; algorithms should be able to certify and convince engineers to approve recommended actions. Explainable AI (XAI) has emerged as a potential solution, providing some of the logic on how the output is derived from the input given, which may help users understand the diagnostic result of the algorithm. In order to inspire and advise data scientists and engineers who are about to develop and use AI approaches in fuel systems, this paper explores the literature of experiment, simulation, and AI-based diagnostics for the fuel system to make an informed statement as to the progress that has been made in intelligent fault diagnostics for fuel systems, emphasizing the necessity of giving unambiguous engineering signals to maintainers, as well as highlighting potential areas for future research.

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Section: Fuel System Simulationmentioning

confidence: 99%

Intelligent Fault Diagnosis of an Aircraft Fuel System Using Machine Learning—A Literature Review

King

Jennions

2023

Machines

View full text Add to dashboard Cite

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Evolution and Tendencies in Design and Exploitation of Cutting Tools and Processes

Dragoi

2025

Mechanisms and Machine Science

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Dynamic simulation whole process optimization and experimental verification of milling aviation aluminum alloy aircraft structure

Cited by 2 publications

References 26 publications

Intelligent Fault Diagnosis of an Aircraft Fuel System Using Machine Learning—A Literature Review

Intelligent Fault Diagnosis of an Aircraft Fuel System Using Machine Learning—A Literature Review

Evolution and Tendencies in Design and Exploitation of Cutting Tools and Processes

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