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International audience; In this paper, computer vision enables recommending a reduced order model for fast stress prediction according to various possible loading environments. This approach is applied on a macroscopic part by using a digital image of a mechanical test. We propose a hybrid approach that simultaneously exploits a data-driven model and a physics-based model, in mechanics of materials. During a machine learning stage, a classification of possible reduced order models is obtained through a clustering of loading environments by using simulation data. The recognition of the suitable reduced order model is performed via a convolutional neural network (CNN) applied to a digital image of the mechanical test. The CNN recommend a convenient mechanical model available in a dictionary of reduced order models. The output of the convolutional neural network being a model, an error estimator, is proposed to assess the accuracy of this output. This article details simple algorithmic choices that allowed a realistic mechanical modeling via computer vision.

The analysis by diffraction of polycrystalline materials can determine the full tensor of the elastic strains within them. Point-by-point maps of elastic strain can thus be obtained in fine-grained engineering alloys, typically using synchrotron X-rays or neutrons. In this paper, a novel approach is presented to calculate the elastic strain energy release rate of a loaded crack from two-dimensional strain maps that are obtained by diffraction. The method is based on a Finite Element approach, which uses diffraction data to obtain the parameters required to calculate the J-integral via the contour integral method. The J integral is robust to uncertainties in the crack tip position and to poor definition of the field in the crack vicinity, and does not rely on theoretical assumptions of the field shape. A validation of the technique is presented using a synthetic dataset from a finite element model. Its experimental application is demonstrated in an analysis of a synchrotron X-ray diffraction strain map for a loaded fatigue crack in a bainitic steel.

Digital image correlation has been routinely used to measure full-field displacements in many areas of solid mechanics, including fracture mechanics. Accurate segmentation of the crack path is needed to study its interaction with the microstructure and stress fields, and studies of crack behaviour, such as the effect of closure or residual stress in fatigue, require data on its opening displacement. Such information can be obtained from any digital image correlation analysis of cracked components, but it collection by manual methods is quite onerous, particularly for massive amounts of data. We introduce the novel application of Phase Congruency to detect and quantify cracks and their opening. Unlike other crack detection techniques, Phase Congruency does not rely on adjustable threshold values that require user interaction, and so allows large datasets to be treated autonomously. The accuracy of the Phase Congruency based algorithm in detecting cracks is evaluated and compared with conventional methods such as Heaviside function fitting. As Phase Congruency is a displacement-based method, it does not suffer from the noise intensification to which gradient-based methods (e.g. strain thresholding) are susceptible. Its application is demonstrated to experimental data for cracks in quasi-brittle (Granitic rock) and ductile (Aluminium alloy) materials.

© 2017 The Author(s)A novel method has been developed based on the conjoint use of digital image correlation to measure full field displacements and finite element simulations to extract the strain energy release rate of surface cracks. In this approach, a finite element model with imported full-field displacements measured by DIC is solved and the J-integral is calculated, without knowledge of the specimen geometry and applied loads. This can be done even in a specimen that develops crack tip plasticity, if the elastic and yield behaviour of the material are known. The application of the method is demonstrated in an analysis of a fatigue crack, introduced to an aluminium alloy compact tension specimen (Al 2024, T351 heat condition).

Recent developments of synchrotron X-ray sources and dedicated high-energybeamlines are now enabling strain measurements from large volumes of industrially relevant metallic materials. Such capability is allowing the validation of novel and alternative non-destructive experimental methods of strain measurement or computational models of complex deformation processes. This study describes the first dynamic contact strain measurement of a ball bearing using stroboscopic Energy Dispersive X-ray Diffraction (EDXD). The experiment probed the dynamic contact strain in the outer raceway of a test bearing. The inner raceway of the bearing was attached to a shaft rotating at 150 revolutions per minute and the outer raceway, where the measurements were made, was fixed in a stationary bearing housing. A triggering system was used to synchronise the data acquisition of the EDXD detector with the bearing rotation. Specifically, diffraction data was acquired, stroboscopically, from the material volume within the raceway, in a known location, when the ball was positioned directly below it. A total of 20 seconds of accumulated diffraction signal was recorded, acquiring 2 milliseconds of data per revolution, providing diffraction patterns of sufficient quality for the dynamic contact strain to be measured. Macromechanical stress field was calculated from the micromechanicalstrains measured from five lattice planes. This allowed a comparison of theexperimentally measured stress field and that of finite element (FE) simulations. Good agreement was observed between the finite element results and experimental measurements indicating the applicability of this novel dynamic strain measurement technique for tribological systems.