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  • Öğe
    A simple manufacturing process of the miniaturised octopus-inspired underwater soft robotic grippers
    (Taylor and Francis Ltd., 2022) Eroğlu, Murat; Şam Parmak, Ebru Devrim
    In this article, we show a new fabrication technique based on CNC machining for the miniaturised octopus-inspired underwater soft robotic grippers. This method provides practical and scale-up production of these grippers. Polydimethylsiloxane/polyethylene terephthalate (PDMS/PET) film (similar to 7 x 7 cm(2)) consisting of concave structures in two different geometries (outer and inner) with the suction-based property was produced by using our suggested manufacturing method. The highest adhesion force was obtained on the flat deformable object by the outer concave structured gripper (similar to 6 kPa) and the flat rigid object by the inner concave structured gripper (similar to 12 kPa). Moreover, both the grippers exhibit switchable adhesion by changing the retraction velocity as well as high repeatability (over 100 cycles) in underwater conditions. This method will enable practical fabrication of the miniaturised octopus-inspired underwater soft robotic grippers. The proposed manufacturing technique will facilitate the widespread use and commercialisation of the grippers.
  • Öğe
    Effect of the Mn Amount on the Structural, Thermal, and Magnetic Properties of Rapidly Solidified (87-x)Cu-13Al-xMn (wt.%) Alloy Ribbons
    (Springer, 2021) Sünbül S.E.; İçin K.; Eroğlu, Murat; Öztürk S.
    In this study, rapidly solidified Cu-Al-Mn ribbons containing 13% Al and 1-14% Mn alloying element by weight were produced by melt spinning method. The structural, thermal, and magnetic properties of produced ribbons were investigated. The parent phase was β1’ martensite for melt-spun ribbons containing Mn amount 1-4%, whereas Cu2AlMn cubic phase for melt-spun ribbons containing Mn amount 7-14%. The microstructures of produced Cu-13Al-1Mn, Cu-13Al-2Mn, Cu-13Al-3Mn, and Cu-13Al-4Mn melt-spun ribbons were consisted of a martensite plate and grain. In addition to this, there were coaxial and equaxial shaped Cu2AlMn phase grains in Cu-13Al-7Mn, Cu-13Al-9Mn, Cu-13Al-11Mn, and Cu-13Al-14Mn melt-spun ribbons. It was observed homogeneous elemental distribution in all ribbons containing different Mn amount. The phase transformation temperatures changed with Mn amount. Austenite-martensite and martensite-austenite phase transformation temperatures decreased with increasing Mn content, while Curie temperatures increased very little with increasing Mn content. Two-way shape memory property was observed for ribbons containing 4 wt.% Mn or less. The magnetic memory effect occurred in the ribbons contains high Mn. The saturation value increased with increasing Mn amount in the produced ribbons.
  • Öğe
    Comparative study of hyperspectral image classification by multidimensional Convolutional Neural Network approaches to improve accuracy
    (Elsevier Ltd, 2021) Ortaç, Gizem; Ozcan G.
    This study presents multidimensional deep learning approaches on hyperspectral images. Storing, processing and interpreting hyperspectral data is very difficult due to its complexity and processing load. Consequently, conventional classifiers are not feasible to extract distinctive features. In order to present efficient classifiers, we utilize deep learning and present Convolutional Neural Network (CNN) approaches. In this study, we evaluate one-dimensional, two-dimensional and three-dimensional convolution model approaches that can present efficient classification performance. Within the scope of the study, samples of widely used hyperspectral data sets are classified by using one-dimensional, two-dimensional and three-dimensional convolutional neural networks by extracting spatial, spectral and spatial-spectral features. All the features provided by hyperspectral sensors are included in the classification by using both separate and joint spectral and spatial features. As a result, our studies have shown that our three-dimensional Convolutional Neural Networks have achieved higher classification rates compared to the state of art models. The accuracy performance of our three dimensional convolutional neural network is able to converge to 100% during classification.
  • Öğe
    Fabrication of micro/nano hydrophobic surfaces by a soft molding method using polyurethane-based elastomer
    (SAGE Publications Ltd, 2021) Eroğlu, Murat; Şam Parmak, Ebru Devrim
    In this research, a stretchable and non-sticky hydrophobic surface is developed using flexible polyurethane-based elastomer Vytaflex 20A. The hierarchical roughness of rose petals was replicated by soft molding method to achieve hydrophobicity. The morphological properties, wettability properties and mechanical properties of replicated micro/nanostructures were characterized. The replicated surface exhibited a similar micro/nanostructure to that of rose petal. Rose petal and replicated surface were highly hydrophobic and their static water contact angles (θ ≈ 124°) are nearly equal. Contact angle hysteresis of the produced surface was measured (10°) to be lower significantly than that of the fresh rose petal (50°). The proposed surface is also highly stretchable having an elongation value of approximately % 700. Such surfaces can be a great candidate for providing easy to clean ability to deformable elastomer products.