Search Results (3)

This article proposes the integration of two novel aspects into the production of 3D-printed customized wrist-hand orthoses. One aspect involves the material, particularly Colorfabb varioShore thermoplastic polyurethane (TPU) filament with an active foaming agent, which allows adjusting the 3D-printed orthoses’ mechanical properties via process parameters such as printing temperature. Consequently, within the same printing process, by using a single extrusion nozzle, orthoses with varying stiffness levels can be produced, aiming at both immobilization rigidity and skin-comfortable softness. This capability is harnessed by 3D-printing the orthosis in a flat shape via material extrusion-based additive manufacturing, which represents the other novel aspect. Subsequently, the orthosis conforms to the user’s upper limb shape after secure attachment, or by thermoforming in the case of a bi-material solution. A dedicated design web app, which relies on key patient hand measurement input, is also proposed, differing from the 3D scanning and modeling approach that requires engineering expertise and 3D scan data processing. The evaluation of varioShore TPU orthoses with diverse designs was conducted considering printing time, cost, maximum flexion angle, comfort, and perceived wrist stability as criteria. As some of the produced TPU orthoses lacked the necessary stiffness around the wrist or did not properly fit the palm shape, bi-material orthoses including polylactic acid (PLA) inserts of varying sizes were 3D-printed and assessed, showing an improved stiffness around the wrist and a better hand shape conformity. The findings demonstrated the potential of this innovative approach in creating bi-material upper limb orthoses, capitalizing on various characteristics such as varioShore properties, PLA thermoforming capabilities, and the design flexibility provided by additive manufacturing technology. Full article

This research examines the flexural fatigue response of 3D-printed foot orthoses produced from various thermoplastic polyurethane (TPU) filaments, including Filaflex 60A, Filaflex 70A, Filaflex 82A, PolyFlex 90A, and varioShore. To subject the insoles to repeated flexion in the metatarsophalangeal area, specialized equipment was developed. A real-world testing scenario was applied to the Filaflex 82A insole, demonstrating that it can sustain over 1,400,000 steps over several months of normal walking (a cadence of approximately 120 steps per minute). Consequently, the experimental conditions were adjusted to double this pace to obtain pertinent results within a shorter testing timeframe. The insoles were subjected to 250 cycles per minute at constant clamping pressures of 176 kPa in the forefoot region. The objective of the evaluation was to determine if 700,000 testing cycles, equivalent to more than two and a half months of daily walking, would induce any damages in the internal structure (infill failure) or external condition (delamination, cracks) of the insoles. Except for compression marks, particularly notable on the foamed material (varioShore TPU) within the clamping zones of the testing device, none of the tested insoles exhibited any signs of external damage after 700,000 cycles. Moreover, the deformations observed in the insoles were non-permanent and nearly entirely disappeared within a few days of rest. The only insole that displayed deterioration of the infill structure was a TPU 82A insole that had been previously worn and then left on a shelf for approximately one year in uncontrolled conditions before being tested at repeated flexion on the apparatus. Additionally, a fifteen-minute walking test was carried out to assess the comfort of each insole, and it was found that the varioShore model, which had a 20% infill density and was 3D-printed at a temperature of 220 °C, stood out as the most comfortable among the tested insoles. Full article

Recent drastic reductions in the Arctic sea-ice cover have raised an interest in understanding the role of sea ice in the global system as well as pointed out a need to understand the physical processes that lead to such changes. Satellite remote-sensing data provide important information about remote ice areas, and Synthetic Aperture Radar (SAR) data have the advantages of penetration of the omnipresent cloud cover and of high spatial resolution. A challenge addressed in this paper is how to extract information on sea-ice types and sea-ice processes from SAR data. We introduce, validate and apply geostatistical and statistical approaches to automated classification of sea ice from SAR data, to be used as individual tools for mapping sea-ice properties and provinces or in combination. A key concept of the geostatistical classification method is the analysis of spatial surface structures and their anisotropies, more generally, of spatial surface roughness, at variable, intermediate-sized scales. The geostatistical approach utilizes vario parameters extracted from directional vario functions, the parameters can be mapped or combined into feature vectors for classification. The method is flexible with respect to window sizes and parameter types and detects anisotropies. In two applications to RADARSAT and ERS-2 SAR data from the area near Point Barrow, Alaska, it is demonstrated that vario-parameter maps may be utilized to distinguish regions of different sea-ice characteristics in the Beaufort Sea, the Chukchi Sea and in Elson Lagoon. In a third and a fourth case study the analysis is taken further by utilizing multi-parameter feature vectors as inputs for unsupervised and supervised statistical classification. Field measurements and high-resolution aerial observations serve as basis for validation of the geostatistical-statistical classification methods. A combination of supervised classification and vario-parameter mapping yields best results, correctly identifying several sea-ice provinces in the shore-fast ice and the pack ice. Notably, sea ice does not have to be static to be classifiable with respect to spatial structures. In consequence, the geostatistical-statistical classification may be applied to detect changes in ice dynamics, kinematics or environmental changes, such as increased melt ponding, increased snowfall or changes in the equilibrium line. Full article