Impact of microstructure, temperature and strain ratio on energy-based low- cycle fatigue life prediction models for TiAl alloys

In this paper, two fatigue lifetime prediction models are tested on TiAl intermetallic using results from uniaxial low-cycle fatigue tests. Both assessments are based on dissipated energy but one of them considers a hydrostatic pressure correction. This work allows to confirm, on this kind of material, the linear nature, already noticed on silicon molybdenum cast iron, TiNi shape memory alloy and 304L stainless steel, of dissipated energy, corrected or not with hydrostatic pressure, according to the number of cycles to failure. This study also highlights that, firstly, the dissipated energy model is here more adequate to estimate low-cycle fatigue life and that, secondly, intrinsic parameters like microstructure as well as extrinsic parameters like temperature or strain ratio have an impact on prediction results.Comment: Attention cette version est une version pr\'e-print (1\'ere version envoy\'ee

Deformable Part-based Fully Convolutional Network for Object Detection

Existing region-based object detectors are limited to regions with fixed box geometry to represent objects, even if those are highly non-rectangular. In this paper we introduce DP-FCN, a deep model for object detection which explicitly adapts to shapes of objects with deformable parts. Without additional annotations, it learns to focus on discriminative elements and to align them, and simultaneously brings more invariance for classification and geometric information to refine localization. DP-FCN is composed of three main modules: a Fully Convolutional Network to efficiently maintain spatial resolution, a deformable part-based RoI pooling layer to optimize positions of parts and build invariance, and a deformation-aware localization module explicitly exploiting displacements of parts to improve accuracy of bounding box regression. We experimentally validate our model and show significant gains. DP-FCN achieves state-of-the-art performances of 83.1% and 80.9% on PASCAL VOC 2007 and 2012 with VOC data only.Comment: Accepted to BMVC 2017 (oral