Localization and estimation of bending and twisting loads using neural networks

Bilal, Diyar Khalis and Ünel, Mustafa and Yıldız, Mehmet and Koç, Bahattin (2020) Localization and estimation of bending and twisting loads using neural networks. In: 46th Annual Conference of the IEEE Industrial Electronics Society (IECON 2020), Singapore

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Abstract

In this paper a neural network based modeling approach is proposed for localization and estimation of loads acting on aircraft wings from full field depth measurements. These measurements can be provided by a multitude of sensors such as depth cameras. Depth cameras have many advantages over other intensity sensors in that they can work in low light conditions and they are invariant to texture and color changes. First, an autoencoder is proposed to extract maximum informative data from the depth images and encode them at a much smaller dimension. Next, to develop the models for localization and estimation of loads, supervised multinomial classification and logistic regression networks are proposed, where the encoded depth features are utilized as input in both networks. The performance of the proposed method is validated on a composite wing subject to concentrated and distributed loads, during which the proposed methods for localization and estimation of loads achieved high accuracies of 90.6% and 90.5%, respectively.
Item Type: Papers in Conference Proceedings
Subjects: T Technology > TJ Mechanical engineering and machinery > TJ163.12 Mechatronics
Divisions: Integrated Manufacturing Technologies Research and Application Center
Faculty of Engineering and Natural Sciences > Academic programs > Mechatronics
Sabancı University Nanotechnology Research and Application Center
Faculty of Engineering and Natural Sciences
Depositing User: Mustafa Ünel
Date Deposited: 22 Sep 2020 16:58
Last Modified: 08 Aug 2023 12:45
URI: https://research.sabanciuniv.edu/id/eprint/41033

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