Low cycle fatigue life improvement of AISI 304 by initial and intermittent wire brush hammering

The effects of hammering by wire brush as a method of improving low cycle fatigue life of highly ductile austenitic stainless steel AISI 304 have been investigated through an experimental study combining imposed strain fatigue tests and assessment of surface characteristic changes under cyclic loading by SEM examinations and XRD analysis. It has been shown that the fatigue life of wire brush hammered surface was increased by 307% at an imposed strain rate of 0.2% and only 17% at an imposed strain rate of 0.5%, comparatively to the turned surface. This increase in fatigue life is explained in terms of fatigue damage that is related to crack networks characteristics and stability which are generated during fatigue on both turned and wire brush hammered surfaces. The improvement of brushed surface is attributed to the role of the surface topography, the near surface stabilized compressive residual stresses and superfi-cial cold work hardening on the fatigue crack network nucleation and growth. It is found that wire brush hammering produces a surface texture that favors, under cyclic loading, nucleation of randomly dispersed short cracks of the order of 40 lm in length stabilized by the compressive residual stress field that reached a value of r0 = 749 MPa. In contrast, turned surface showed much longer unstable cracks of the order of 200 lm in length nucleated in the machining groves with high tendency to propagate under the effect of tensile residual stress field that reached value of r0 = 476 MPa. This improvement is limited to strain rates lower than 0.5%. At higher strain rates, a cyclic plastic deformation induced martensitic phase alters furthermore the fatigue behavior by producing high cyclic strengthening of the bulk mate-rial. This phenomenon lead to a reduction in strain imposed fatigue life. It has also been established that wire brush hammering can be used as an onsite surface treatment to improve the residual fatigue life of components subjected to cyclic loading. The efficiency of this treatment is demonstrated if it is performed at a fraction of service lifetime Ni/Nr lower than 0.5

A Hybrid Privacy-Preserving Deep Learning Approach for Object Classification in Very High-Resolution Satellite Images

Deep learning (DL) has shown outstanding performances in many fields, including remote sensing (RS). DL is turning into an essential tool for the RS research community. Recently, many cloud platforms have been developed to provide access to large-scale computing capacity, consequently permitting the usage of DL architectures as a service. However, this opened the door to new challenges associated with the privacy and security of data. The RS data used to train the DL algorithms have several privacy requirements. Some of them need a high level of confidentiality, such as satellite images related to public security with high spatial resolutions. Moreover, satellite images are usually protected by copyright, and the owner may strictly refuse to share them. Therefore, privacy-preserving deep learning (PPDL) techniques are a possible solution to this problem. PPDL enables training DL on encrypted data without revealing the original plaintext. This study proposes a hybrid PPDL approach for object classification for very-high-resolution satellite images. The proposed encryption scheme combines Paillier homomorphic encryption (PHE) and somewhat homomorphic encryption (SHE). This combination aims to enhance the encryption of satellite images while ensuring a good runtime and high object classification accuracy. The method proposed to encrypt images is maintained through the public keys of PHE and SHE. Experiments were conducted on real-world high-resolution satellite images acquired using the SPOT6 and SPOT7 satellites. Four different CNN architectures were considered, namely ResNet50, InceptionV3, DenseNet169, and MobileNetV2. The results showed that the loss in classification accuracy after applying the proposed encryption algorithm ranges from 2% to 3.5%, with the best validation accuracy on the encrypted dataset reaching 92%