Stainless steel bars are currently used to reinforce large concrete structures when they need to guarantee a reliable service in saline environments. As these structures are also usually submitted to cyclic loads, their fatigue performance is an important issue to take into account. It is also well known that shot peening is a process largely employed to improve the fatigue behaviour of metal products. In this process the metallic surface of a component is peened with small spherical shots in order to induce plastic deformations which generate compressive residual stresses and, consequently, the component fatigue resistance is significantly enhanced. AISI 2205 duplex stainless steel bars, already largely used for concrete reinforcement, was the material choice in this work. The bars were hot rolled and afterwards different shot peening treatments were applied, which were fully characterised by means of Almen intensity and coverage ratio. Residual stresses were also measured by means of Xray diffraction. The S-N fatigue curves of the bars submitted to the different shot peening treatments were determined and the improvement due to shot peening explained taking into account the shot peening effects on the surface of the bars.
The influence of shot peening on the fatigue properties of duplex stainless steel reinforcing bars manufactured using both hot‐ and cold‐rolled processes was studied. The S‐N curves of the bars before and after the shot‐peening process were determined, showing that shot peening improves the fatigue behaviour of the rebars. This improvement is essentially due to the introduction of a compressive residual stress field in the surface of the reinforcing bars, but also to the smoothing of the surface flaws and cold working generated during the manufacturing process. This improvement is much greater in the case of the hot‐rolled bars, mainly as a result of their much higher ability for plastic deformation, whereas cold‐rolled bars had a much higher hardness. A more severe peening action capable of promoting greater plastic deformation on the bar surface is judged necessary to improve the fatigue resistance of cold‐rolled rebars.
The detection of pulmonary nodules is one of the most studied problems in the field of medical image analysis due to the great difficulty in the early detection of such nodules and their social impact. The traditional approach involves the development of a multistage CAD system capable of informing the radiologist of the presence or absence of nodules. One stage in such systems is the detection of ROI (regions of interest) that may be nodules in order to reduce the space of the problem. This paper evaluates fuzzy clustering algorithms that employ different classification strategies to achieve this goal. After characterising these algorithms, the authors propose a new algorithm and different variations to improve the results obtained initially. Finally it is shown as the most recent developments in fuzzy clustering are able to detect regions that may be nodules in CT studies. The algorithms were evaluated using helical thoracic CT scans obtained from the database of the LIDC (Lung Image Database Consortium).
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