Characterization of impact induced damage modes in single lap joints of woven GFRP composites

Single lap joints of woven GFRP composites have been investigated for impact induced damage modes using C-scan, X-ray micro tomography, imaging and finite element (FE) modelling. This has allowed for damage modes to be observed in 3D from macro to micro level-resulting in much better understanding of damage mechanisms and realistic FE modelling.

Transverse cracking of a simple lap joint under axial load

The failure mode of axially loaded simple, single lap joints formed between thin adherends which are flexible in bending is conventionally described as one of axial peeling. We have observed - using high-speed photography - that it is also possible for failure to be preceded by the separation front, or crack, moving in a transverse direction, i.e. perpendicular to the direction of the axial load. A simple energy balance analysis suggests that the critical load for transverse failure is the same as that for axial separation for both flexible lap joints, where the bulk of the stored elastic energy lies in the adhesive, and structural lap joints in which the energy stored in the adherends dominates. The initiation of the failure is dependent on a local increases in either stress or strain energy to some critical values. In the case of a flexible joint, this will occur within the adhesive layer and the critical site will be close to one of the corners of the joint overlap from which the separation front can proceed either axially or transversely. These conclusions are supported by a finite element analysis of a joint formed between adherends of finite width by a low modulus adhesive. © 2012 Taylor & Francis.

Parameters identification by a single point free response measurement

When a thin rectangular plate is restrained on the two long edges and free on the remaining edges, the equivalent stiffness of the restraining joints can be identified by the order of the natural frequencies obtained using the free response of the plate at a single location. This work presents a method to identify the equivalent stiffness of the restraining joints, being represented as simply supporting the plate but elastically restraining it in rotation. An integral transform is used to map the autospectrum of the free response from the frequency domain to the stiffness domain in order to identify the equivalent torsional stiffness of the restrained edges of the plate and also the order of natural frequencies. The kernel of the integral transform is built interpolating data from a finite element model of the plate. The method introduced in this paper can also be applied to plates or shells with different shapes and boundary conditions. © 2011 Elsevier Ltd. All rights reserved.

Single camera pose estimation using Bayesian filtering and Kinect motion priors

Traditional approaches to upper body pose estimation using monocular vision rely on complex body models and a large variety of geometric constraints. We argue that this is not ideal and somewhat inelegant as it results in large processing burdens, and instead attempt to incorporate these constraints through priors obtained directly from training data. A prior distribution covering the probability of a human pose occurring is used to incorporate likely human poses. This distribution is obtained offline, by fitting a Gaussian mixture model to a large dataset of recorded human body poses, tracked using a Kinect sensor. We combine this prior information with a random walk transition model to obtain an upper body model, suitable for use within a recursive Bayesian filtering framework. Our model can be viewed as a mixture of discrete Ornstein-Uhlenbeck processes, in that states behave as random walks, but drift towards a set of typically observed poses. This model is combined with measurements of the human head and hand positions, using recursive Bayesian estimation to incorporate temporal information. Measurements are obtained using face detection and a simple skin colour hand detector, trained using the detected face. The suggested model is designed with analytical tractability in mind and we show that the pose tracking can be Rao-Blackwellised using the mixture Kalman filter, allowing for computational efficiency while still incorporating bio-mechanical properties of the upper body. In addition, the use of the proposed upper body model allows reliable three-dimensional pose estimates to be obtained indirectly for a number of joints that are often difficult to detect using traditional object recognition strategies. Comparisons with Kinect sensor results and the state of the art in 2D pose estimation highlight the efficacy of the proposed approach.