Hynek Lauschmann

Fractography of crack networks in thin layers

Abstract The subject of this article is detection and description of random networks of cracks on a material surface. Thin layers of (Ti,Al)N on a hardmetal (WC–6%Co) substrate were subjected to thermomechanical loads by laser irradiation. Cracks in the coating caused by this load were examined by scanning electron microscopy (SEM). A special method for automatical detection of cracks in SEM images of the surface is based on the application of edge detection and sequential testing of the course of individual cracks. The system of cracks is classified into branches with respect to passing or finishing in knots. To derive the characteristics of the network, a regression model of branches with user-specified maximum of represented curvature is suggested. As an example of the output, the distribution of the local crack direction conditional on the crack length is shown.

Multi-Fractal Features of Fatigue Crack Surfaces in Relation to Crack Growth Rate

Texture of a fatigue crack surface is strictly related to crack growth rate. Cracks in specimens from aluminum alloy were studied. Two types of information were used: SEM images of fracture surfaces, and 3D reconstructions of fracture surface morphologies. Sets of equidistantly focused images obtained by an optical microscope served as the basis for 3D reconstruction. Multiparametric fractal analysis was applied to characterize crack surfaces. A vector of fractal features represented each image or 3D reconstruction of selected locations of fracture surfaces. For estimating fractal characteristics, the box-counting method in 3D was used in all cases, [1]. Multilinear regression was used to express the relation between crack growth rate and feature vectors, with satisfactory results for both crack surface representations.

Textural Fractography of Fatigue Failures under Variable Cycle Loading

A new concept of counting time at fatigue processes is proposed, aimed to reach fractographic compatibility in cases of different loading sequences. Values of cycle effectivity are summarized to give the new reference time. The improvement is shown in application - textural fractography of three specimens loaded by constant cycle, constant cycle with periodic overloading, and a random block, respectively. In contrast to the conventional crack growth rate, the reference crack growth rate is related to common morphologic features of all fracture surfaces.

A Contribution to the Physical Interpretation of the Morphology of Fatigue Fracture Surfaces

The reference texture is a subset of the image texture in SEM fractographs of fatigue fractures. It is common to all fractures caused by loadings in which significant events occur sufficiently regularly and frequently. The reference crack growth rate is unambiguously related to the reference texture. A particular loading is characterized by the ratio of the reference and conventional crack growth rates called reference factor. Its value may be related to the sequence of successive sizes of cyclic plastic zone, while the mechanism of the effect of overloads follows the models of Wheeler and Willenborg. Application to a set of three test specimens from stainless steel AISI 304L loaded by various loading regimes is shown.

Model-Based Fatique Fractographs Texture Analysis

A novel model-based approach for estimation of the velocity of crack growth from microfractographical images is proposed. These images are represented by a Gaussian Markov random field model and the crack growth rate is modelled by a linear regression model in the Gaussian-Markov parameter space. The method is numerically very efficient because both crack growth rate model parameters as well as the underlying random field model parameters are estimated using fast analytical estimators.

Spatial Statistics in the Material Research

Spatial statistics differs from classical statistics in many aspects, e.g. correlated data samples, edge effects, variety of sampling schemes. In the present paper we emphasize the two-stage character of spatial data evaluation. A standard approach is that one first transforms the observed image pattern and then does statistical inference and interprets the results. We present two classes of transformation both followed by regression. In the first application stereological unfolding is desired to reconstruct spatial data from planar sections. In the second case a grey-tone image from scanning electron microscopy is evaluated. Results of statistical description are desired in material research, namely in damage modelling of composite materials and fractography in aircraft research and nuclear power industry. AMS 1991 subject classification: Primary 62H10, secondary 60G60