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Prediction of the hardness of hardened specimens with a neural network
Matej Babič, Peter Kokol, Igor Belič, Peter Panjan, Miha Kovačič, Jože Balič, Timotej Verbovšek, 2014, original scientific article

Abstract: In this article we describe the methods of intelligent systems to predict the hardness of hardened specimens. We use the mathematical method of fractal geometry in laser techniques. To optimize the structure and properties of tool steel, it is necessary to take into account the effect of the self-organization of a dissipative structure with fractal properties at a load. Fractal material science researches the relation between the parameters of fractal structures and the dissipative properties of tool steel. This paper describes an application of the fractal dimension in the robot laser hardening of specimens. By using fractal dimensions, the changes in the structure can be determined because the fractal dimension is an indicator of the complexity of the sample forms. The tool steel was hardened with different speeds and at different temperatures. The effect of the parameters of robot cells on the material was better understood by researching the fractal dimensions of the microstructures of hardened specimens. With an intelligent system the productivity of the process of laser hardening was increased because the time of the process was decreased and the topographical property of the material was increased.
Keywords: fractal dimension, fractal geometry, neural network, prediction, hardness, steel, tool steel, laser
Published in DKUM: 17.03.2017; Views: 2032; Downloads: 120
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Characterization of corrosion processes by current noise wavelet-based fractaland correlation analysis
Peter Planinšič, Aljana Petek, 2008, original scientific article

Abstract: Electrochemical noise data in the presence of pitting, general corrosion and passivity were analyzed using the discrete wavelet transform. The registered current noise was decomposed into a set of band-limited details, which contain information about corrosion events occurring at a determined time-scale. It has been observed that the signal variance and variances of details depend on the intensity of processes. Distribution of the signal energy among different details was characteristic for the particular type of corrosion. The characterization of corrosion processes on the basis of in the wavelet domain calculated Hurst parameter H and fractal dimension, D, of electrochemical noise signals has been established. It is concluded that general corrosion is a stationary random process with a weak persistence and D= 2.14, whereas pitting corrosion is a non-stationary process with a long memory effect and D = 1.07. Passivity is a non-stationary process near to the Brownian motion with D = 1.56. The persistence features of electrochemical noise signals were explained also by correlation coefficients calculated between signals obtained by discrete wavelet multiresolution decomposition.
Keywords: elektrokemijski šum, valčki, Hurstov parameter, fraktalna dimenzija, korelacijski koeficient, korozija, electrochemical noise, wavelets, Hurst parameter, fractal dimension, correlation coefficients, corrosion
Published in DKUM: 31.05.2012; Views: 2338; Downloads: 89
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