Precision Fibre Angle Inspection for Carbon Fibre Composite Structures Using Polarisation Vision

This paper evaluates the precision of polarisation imaging technology for the inspection of carbon fibre composite components. Specifically, it assesses the feasibility of the technology for fibre orientation measurements based on the premise that light is polarised by reflection from such anisotropically conductive surfaces. A recently commercialised Sony IMX250MZR sensor is used for data capture by using various lighting conditions. The paper shows that it is possible to obtain sub-degree accuracy for cured and dry woven and unidirectional materials in ideal conditions, which comprised dark field illumination. Indeed, in ideal conditions, the average relative angles can be measured to an accuracy of 0.1–0.2°. The results also demonstrate a precision of the order 1° for more general illumination, such as dome illumination and ambient lighting, for certain material type/lens combinations. However, it is also shown that the precision varies considerably depending on illumination, lens choice and material type, with some results having errors above 2°. Finally, a feasibility study into the inspection of three-dimensional components suggests that only limited application is possible for non-planar regions without further research. Nevertheless, the observed phenomena for such components are, at least, qualitatively understood based on physics theory.

Shear-induced chemical segregation in a Fe-based bulk metallic glass at room temperature

Shear-induced segregation, by particle size, is known in the flow of colloids and granular media, but is unexpected at the atomic level in the deformation of solid materials, especially at room temperature. In nanoscale wear tests of an Fe-based bulk metallic glass at room temperature, without significant surface heating, we find that intense shear localization under a scanned indenter tip can induce strong segregation of a dilute large-atom solute (Y) to planar regions that then crystallize as a Y-rich solid solution. There is stiffening of the material, and the underlying chemical and structural effects are characterized by transmission electron microscopy. The key influence of the soft Fe–Y interatomic interaction is investigated by ab-initio calculation. The driving force for the induced segregation, and its mechanisms, are considered by comparison with effects in other sheared media.

Exact and approximate similarities of non-necessarily rational planar, parametrized curves, using centers of gravity and inertia tensors

We provide an algorithm to detect whether two bounded, planar parametrized curves are similar, i.e. whether there exists a similarity transforming one of the curves onto the other. The algorithm is valid for completely general parametrizations, and can be adapted to the case when the input is given with finite precision, using the notion of approximate [Formula: see text]. The algorithm is based on the computation of centers of gravity and inertia tensors of the considered curves or of the planar regions enclosed by the curves, which have nice properties when a similarity transformation is applied. In more detail, the centers of gravity are mapped onto each other, and the matrices representing the inertia tensors satisfy a simple relationship: when the similarity is a congruence (i.e. distances are preserved) the matrices are congruent, and in the more general case the relationship is analogous, but involves the square of the scaling constant. Using both properties, and except for certain pathological cases, the similarities can be found. Additional ideas are presented for the case of closed, i.e. compact, curves.

Stable recovery of planar regions with algebraic boundaries in Bernstein form

We present a new method for the stable reconstruction of a class of binary images from a small number of measurements. The images we consider are characteristic functions of algebraic domains, that is, domains defined as zero loci of bivariate polynomials, and we assume to know only a finite set of uniform samples for each image. The solution to such a problem can be set up in terms of linear equations associated to a set of image moments. However, the sensitivity of the moments to noise makes the numerical solution highly unstable. To derive a robust image recovery algorithm, we represent algebraic polynomials and the corresponding image moments in terms of bivariate Bernstein polynomials and apply polynomial-generating, refinable sampling kernels. This approach is robust to noise, computationally fast and simple to implement. We illustrate the performance of our reconstruction algorithm from noisy samples through extensive numerical experiments. Our code is released open source and freely available.