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This paper examines the end-to-end development process for a Convolution Neural Network (CNN) based damage classification tool for ultrasonic inspection of aerospace-grade composite structures. The recent advent of Artificial Intelligence (AI) and Machine Learning (ML) has piqued the interest of the aerospace industry since it has the potential to improve performance and alleviate the burden on personnel. The big question in the industry right now is how and where to introduce this technology while assuring the safety and reliability of its implementation. Guidelines drafted by the European Aviation Safety Agency (EASA) for the development of AI showed that maintenance and training were the most accessible points of entry for this technology as it did not have the same stringent requirements that a flying system would have. This paper proposes a research methodology which allows for the cost-effective development of ultrasonic data for the training and testing of data-driven tools. This was partly achieved by using a novel eFlaw technique which has been implemented for the first time in composite structures. The method allows for significant augmentation and generalisation of datasets, resulting in a model with the ability to detect features potentially smaller than one-quarter of a wavelength. This improved performance paves the way for more sensitive low-frequency ultrasonic inspection in thick composites. To evaluate these models, various evaluation techniques were compared and showed that Receiver operator curves and confusion matrix-derived metrics provided comparable results. Explainable methods found that the GradCam and the inspection of feature maps showed the most interpretable results on the features that were being identified. Using the feature maps it was possible to generate a new type of C-scan, called an F-scan (Feature-scan) which provides an inspector with a view of the C-scan from the perspective of a feature map from the model providing an interpretable view of the model’s classifications. In addition to these positive results, this thesis provides readers with a cost-effective methodology for developing data-driven tools for maintenance applications within the aerospace industry.
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This paper examines the end-to-end development process for a Convolution Neural Network (CNN) based damage classification tool for ultrasonic inspection of aerospace-grade composite structures. The recent advent of Artificial Intelligence (AI) and Machine Learning (ML) has piqued the interest of the aerospace industry since it has the potential to improve performance and alleviate the burden on personnel. The big question in the industry right now is how and where to introduce this technology while assuring the safety and reliability of its implementation. Guidelines drafted by the European Aviation Safety Agency (EASA) for the development of AI showed that maintenance and training were the most accessible points of entry for this technology as it did not have the same stringent requirements that a flying system would have. This paper proposes a research methodology which allows for the cost-effective development of ultrasonic data for the training and testing of data-driven tools. This was partly achieved by using a novel eFlaw technique which has been implemented for the first time in composite structures. The method allows for significant augmentation and generalisation of datasets, resulting in a model with the ability to detect features potentially smaller than one-quarter of a wavelength. This improved performance paves the way for more sensitive low-frequency ultrasonic inspection in thick composites. To evaluate these models, various evaluation techniques were compared and showed that Receiver operator curves and confusion matrix-derived metrics provided comparable results. Explainable methods found that the GradCam and the inspection of feature maps showed the most interpretable results on the features that were being identified. Using the feature maps it was possible to generate a new type of C-scan, called an F-scan (Feature-scan) which provides an inspector with a view of the C-scan from the perspective of a feature map from the model providing an interpretable view of the model’s classifications. In addition to these positive results, this thesis provides readers with a cost-effective methodology for developing data-driven tools for maintenance applications within the aerospace industry.
Journal article(2019)
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Alexander Baer, Nils Horbelt, Marlies Nijemeisland, Santiago J. Garcia, Peter Fratzl, Stephan Schmidt, Georg Mayer, Matthew J. Harrington
Natural materials provide an increasingly important role model for the development and processing of next-generation polymers. The velvet worm Euperipatoides rowelli hunts using a projectile, mechanoresponsive adhesive slime that rapidly and reversibly transitions into stiff glassy polymer fibers following shearing and drying. However, the molecular mechanism underlying this mechanoresponsive behavior is still unclear. Previous work showed the slime to be an emulsion of nanoscale charge-stabilized condensed droplets comprised primarily of large phosphorylated proteins, which under mechanical shear coalesce and self-organize into nano- and microfibrils that can be drawn into macroscopic fibers. Here, we utilize wide-angle X-ray diffraction and vibrational spectroscopy coupled with in situ shear deformation to explore the contribution of protein conformation and mechanical forces to the fiber formation process. Although previously believed to be unstructured, our findings indicate that the main phosphorylated protein component possesses a significant β-crystalline structure in the storage phase and that shear-induced partial unfolding of the protein is a key first step in the rapid self-organization of nanodroplets into fibers. The insights gained here have relevance for sustainable production of advanced polymeric materials.
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Natural materials provide an increasingly important role model for the development and processing of next-generation polymers. The velvet worm Euperipatoides rowelli hunts using a projectile, mechanoresponsive adhesive slime that rapidly and reversibly transitions into stiff glassy polymer fibers following shearing and drying. However, the molecular mechanism underlying this mechanoresponsive behavior is still unclear. Previous work showed the slime to be an emulsion of nanoscale charge-stabilized condensed droplets comprised primarily of large phosphorylated proteins, which under mechanical shear coalesce and self-organize into nano- and microfibrils that can be drawn into macroscopic fibers. Here, we utilize wide-angle X-ray diffraction and vibrational spectroscopy coupled with in situ shear deformation to explore the contribution of protein conformation and mechanical forces to the fiber formation process. Although previously believed to be unstructured, our findings indicate that the main phosphorylated protein component possesses a significant β-crystalline structure in the storage phase and that shear-induced partial unfolding of the protein is a key first step in the rapid self-organization of nanodroplets into fibers. The insights gained here have relevance for sustainable production of advanced polymeric materials.
