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An algorithm of crack detection during fatigue testing of materials, designed to automate the process of cyclic loading and tracking the crack tip, is proposed and tested. The ultimate goal of the study is aimed at controlling the displacements of the optical system with regard to the specimen under fatigue loading to ensure observation of the 'area of interest'. It is shown that the image region that contains the crack may be detected and positioned with an average error of 1.93%. In terms of determining the crack tip position, the algorithm provides the accuracy of its localization with the average error value of 56 pixels.

During cyclic test of carbon fiber reinforce polymers it was investigated the influence of cycling number until fracture and notch depth on the process of relief forming on the “smart sensor” surface. “Smart sensor” is a polycrystalline aluminum foil glued on the object and deforming jointly together. Relief registration process is done by digital photo camera Canon 550D, embedded in optical path of microscope. There are four informative parameters are obtained from the images - mean square error, information entropy, Fourier energy spectrum and fractal dimension. Feature and staging of the relief forming process are investigated and discussed in this paper. The obtained data could be applied for designing the built-in control of composite structures and details.

A combined method to investigation of localized deformation processes in notched aluminum alloy as well as carbon fiber reinforced composite specimens is applied in order to reveal characteristic stages of strain and fracture. Stress concentrators have the shape of a circular hole and edge crack of various sizes. Carbon fiber reinforced composites (CFRC) with notches of different size were also tested in order to illustrate the difference in deformation behavior between metal and reinforced polymeric composites specimens. Use of simultaneous strain data registration has allowed us to register and compare parameters under analysis during entire time of the experiments. The reasons of similarity and difference of the results are shown and discussed. It is offered to apply the obtained results for the aims of non-destructive testing of structural materials being based on revealing characteristic stages of strain development and a particularly prefracture stage.

A cyclic tension and alternating bending tests of 12Cr1MoV steel specimens in as-supplied state and after surface nanostructuring by Zr+ ion beam have been carried out. Distinctions in formation of strain induced relief, as well as the cracking pattern of modified surface layer are illustrated by methods of optical microscopy and interferential profilometry. Changes to occur in subsurface layer are characterized by means of nanoindentation and fractography observation of fracture surfaces. The description of differences of deformation behavior is performed with use of the multiple cracking concept.

Various criteria for processing of aluminum foil ''smart sensors'' images for fatigue evaluation of carbon fiber reinforced polymer (CFRP) were analyzed. There are informative parameters used to assess image quality and surface relief and accordingly to characterize the fatigue damage state of CFRP. The sensitivity of all criteria to distortion influences, particularly, to Gaussian noise, blurring and JPEG compression was investigated. The main purpose of the research is related to the search of informative parameters for fatigue evaluation, which are the least sensitive to different distortions.

For the development of a technique for cyclic deformation assessment using aluminum foil sensors glued onto the specimen surface the fatigue tests of carbon fiber reinforced polymer (CFRP) specimens were carried out. The DSLR camera mounted onto an optical microscope was used for capturing the images of sensors to reflect strain induced relief, which than was numerically estimated using various informative parameters in order to obtain the cyclic deformation assessment of composite. The results are discussed in view of application of this method for the development of Structural Health Monitoring (SHM) approach.