Applying Neural Networks to the F-35 Seam Validation Process

Martínez, Julián; MacAllister, Anastacia; Dominguez, Edgar; Mahlmann, Curtis; Quinlan, Grace; McKee, Matthew

doi:10.1109/aero50100.2021.9438381

Cited by 2 publications

(3 citation statements)

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“…The predominant ML algorithms in this layer are the so-called deep ML algorithms, since these algorithms give better results with a large volume of data and/or unstructured data [69]. These algorithms are based on neural networks [79]. If the number of layers of such a network is high, they are called deep learning, e.g., these algorithms are applied to military subjects in [72,80,81].…”

Section: Discussionmentioning

confidence: 99%

Military Applications of Machine Learning: A Bibliometric Perspective

2022

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The military environment generates a large amount of data of great importance, which makes necessary the use of machine learning for its processing. Its ability to learn and predict possible scenarios by analyzing the huge volume of information generated provides automatic learning and decision support. This paper aims to present a model of a machine learning architecture applied to a military organization, carried out and supported by a bibliometric study applied to an architecture model of a nonmilitary organization. For this purpose, a bibliometric analysis up to the year 2021 was carried out, making a strategic diagram and interpreting the results. The information used has been extracted from one of the main databases widely accepted by the scientific community, ISI WoS. No direct military sources were used. This work is divided into five parts: the study of previous research related to machine learning in the military world; the explanation of our research methodology using the SciMat, Excel and VosViewer tools; the use of this methodology based on data mining, preprocessing, cluster normalization, a strategic diagram and the analysis of its results to investigate machine learning in the military context; based on these results, a conceptual architecture of the practical use of ML in the military context is drawn up; and, finally, we present the conclusions, where we will see the most important areas and the latest advances in machine learning applied, in this case, to a military environment, to analyze a large set of data, providing utility, machine learning and decision support.

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Section: Discussionmentioning

confidence: 99%

Military Applications of Machine Learning: A Bibliometric Perspective

2022

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“…Thumati et al [12] proposed an implementation scheme utilizing AI-based techniques and big data analytics to reduce human dependability and improve quality control for cost optimization within the manufacturing environment. Another implementation scheme for refining military manufacturing processes by Martinez [13] incorporated a seam validation process with machine learning technology to reduce production costs.…”

Section: Related Workmentioning

confidence: 99%

“…Model from [13] Manufacturing model in [12] Aeronautical Assembly Process (Horizontal Tail Plane Structure) [32]…”

Section: Current Aerospace Manufacturing Modelmentioning

confidence: 99%

Deep Learning-Based Real Time Defect Detection for Optimization of Aircraft Manufacturing and Control Performance

Shafi

Mazahir

Fatima

et al. 2023

Drones

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Monitoring tool conditions and sub-assemblies before final integration is essential to reducing processing failures and improving production quality for manufacturing setups. This research study proposes a real-time deep learning-based framework for identifying faulty components due to malfunctioning at different manufacturing stages in the aerospace industry. It uses a convolutional neural network (CNN) to recognize and classify intermediate abnormal states in a single manufacturing process. The manufacturing process for aircraft factory products comprises different phases; analyzing the components after the integration is labor-intensive and time-consuming, which often puts the company’s stake at high risk. To overcome these challenges, the proposed AI-based system can perform inspection and defect detection and alleviate the probability of components’ needing to be re-manufacturing after being assembled. In addition, it analyses the impact value, i.e., rework delays and costs, of manufacturing processes using a statistical process control tool on real-time data for various manufactured components. Defects are detected and classified using the CNN and teachable machine in the single manufacturing process during the initial stage prior to assembling the components. The results show the significance of the proposed approach in improving operational cost management and reducing rework-induced delays. Ground tests are conducted to calculate the impact value followed by the air tests of the final assembled aircraft. The statistical results indicate a 52.88% and 34.32% reduction in time delays and total cost, respectively.

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Applying Neural Networks to the F-35 Seam Validation Process

Cited by 2 publications

References 10 publications

Military Applications of Machine Learning: A Bibliometric Perspective

Military Applications of Machine Learning: A Bibliometric Perspective

Deep Learning-Based Real Time Defect Detection for Optimization of Aircraft Manufacturing and Control Performance

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