RESUMO
The determination of suitable testing and qualification procedures for fiber-reinforced polymer matrix composite structures is an active area of research due to the increased demand, especially in the field of aerospace. This research illustrates the development of a generic qualification framework for a composite-based main landing gear strut of a lightweight aircraft. For this purpose, a landing gear strut composed of T700 carbon fiber/epoxy material was designed and analyzed for a given lightweight aircraft having mass of 1600 kg. Computational analysis was performed on ABAQUS CAE® to evaluate the maximum stresses and critical failure modes encountered during one-point landing condition as defined in the UAV Systems Airworthiness Requirements (USAR) and Air Worthiness Standards FAA FAR Part 23. A three-step qualification framework including material, process and product-based qualification was then proposed against these maximum stresses and failure modes. The proposed framework revolves around the destructive testing of specimens initially as per ASTM standards D 7264 and D 2344, followed by defining the autoclave process parameters and customized testing of thick specimens to evaluate material strength against the maximum stresses in specific failure modes of the main landing gear strut. Once the desired strength of the specimens was achieved based on material and process qualifications, qualification criteria for the main landing gear strut were proposed which would not only serve as an alternative to drop test the landing gear struts as defined in air worthiness standards during mass production, but would also give confidence to manufacturers to undertake the manufacturing of main landing gear struts using qualified material and process parameters.
RESUMO
Accurate segmentation of the vertebrae from medical images plays an important role in computer-aided diagnoses (CADs). It provides an initial and early diagnosis of various vertebral abnormalities to doctors and radiologists. Vertebrae segmentation is very important but difficult task in medical imaging due to low-contrast imaging and noise. It becomes more challenging when dealing with fractured (osteoporotic) cases. This work is dedicated to address the challenging problem of vertebra segmentation. In the past, various segmentation techniques of vertebrae have been proposed. Recently, deep learning techniques have been introduced in biomedical image processing for segmentation and characterization of several abnormalities. These techniques are becoming popular for segmentation purposes due to their robustness and accuracy. In this paper, we present a novel combination of traditional region-based level set with deep learning framework in order to predict shape of vertebral bones accurately; thus, it would be able to handle the fractured cases efficiently. We termed this novel Framework as "FU-Net" which is a powerful and practical framework to handle fractured vertebrae segmentation efficiently. The proposed method was successfully evaluated on two different challenging datasets: (1) 20 CT scans, 15 healthy cases, and 5 fractured cases provided at spine segmentation challenge CSI 2014; (2) 25 CT image data (both healthy and fractured cases) provided at spine segmentation challenge CSI 2016 or xVertSeg.v1 challenge. We have achieved promising results on our proposed technique especially on fractured cases. Dice score was found to be 96.4 ± 0.8% without fractured cases and 92.8 ± 1.9% with fractured cases in CSI 2014 dataset (lumber and thoracic). Similarly, dice score was 95.2 ± 1.9% on 15 CT dataset (with given ground truths) and 95.4 ± 2.1% on total 25 CT dataset for CSI 2016 datasets (with 10 annotated CT datasets). The proposed technique outperformed other state-of-the-art techniques and handled the fractured cases for the first time efficiently.
