Volume 20, Issue No.1


intelligent systems, fractal dimension, Hybrid system, hardening


Machine learning, concerns the construction and study of systems that can learn from data. Learning is the fundamental and most important element of biological intelligent systems. We use methods of intelligent system in industrial process. Moreover, with fractal geometry, we analysed the complexity of robot laser-hardened patterns. We analysed patterns hardened with different robot laser cell parameters; namely, the two parameters of speed and temperature. Fractal dimensions were calculated with a method for estimating the Hurst exponent H for 3D object. Also, fractal dimensions were used for pattern recognition and for describing the roughness of the hardened patterns. However, owing to the complexity of real-world problems, the development of intelligent learning systems still remains a challenging topic. For the analysis of results, we use an intelligent system method, namely, a neural network, genetic programing and regression. The general goal of Artificial Intelligence and Machine Learning research is to: develop new intelligent methods to make rational decisions based on observations; learn from experience; and automatically extract knowledge and patterns from data. Thus, this paper explores the use a hybrid method to improve existing hybrids. We present a new method for a hybrid system, based on the spiral method.


Acta Mechanica Slovaca. Volume 20, Issue 1, Pages 34 – 40, ISSN 1335-2393


  New Hybrid System Using in Modelling Process of Hardening with Intelligent System


[1] B.B. Mandelbrot. The fractal geometry of Nature. New York: W.H. Freeman, (1982), p. 93.
[2] P.V. Yasnii, P.O. Marushchak, I.V. Konovalenko, R.T. Bishchak, Materials Science 46 (2008) 833.
[3] P.V. Yasnii, P.O. Marushchak, I.V. Konovalenko, R.T. Bishchak, Materials Science 47 (2009) 798.
[4] M. Babič, P. Kokol, M. Milfelner, S. Babič. Fractal structure of robot laser-hardened different material. V: 8th International Summer School/Conference at the University of Maribor, 26 June–10 July 2011, Maribor, Slovenia. ROMANOVSKI, Valery (ur.), ROBNIK, Marko (ur.). Let's face chaos through nonlinear dynamics: 8th International Summer School/Conference at the University of Maribor, 26 June–10 July 2011, Maribor, Slovenia. [Maribor]: CAMTP, (2011), str. 83.
[5] L.W. Fan, Y.C. Hu, T. Tian, Z.T. Yu, The predication of effective thermal conductivities perpendicular to the fibres of wood using a fractal model and an improved transient measurement technique, International Journal of Heat Mass Transfer 49 (2006) 4116–4123.
[6] T. Ficker, Fractal strength of cement gels and universal dimension of fracture surfaces, Theor. Appl. Fract. Mech. 50 (2008) 167–171.
[7] M.Q. Jiang, J.X. Meng, J.B. Gao, X.L. Wang, T. Rouxel, V. Keryvin, Z. Ling, L.H. Dai, Fractal in fracture of bulk metallic glass, Intermetallics 18 (2010) 2468–2471.
[8] A. Carpinteri, S.A. Puzzi, Engineering Fract. Mech. 73 (2006) 2110.
[9] Q. Chang, D.L. Chen, H.Q. Ru, X.Y. Yue, L. Yu, C.P. Zhang, Three-dimensional fractal analysis of fracture surfaces in titanium–iron particulate reinforced hydroxyapatite composites: relationship between fracture toughness and fractal dimension, J. Mater. Sci. 46 (2011) 6118–6123.
[10] A.L. Ahmad, N.N.N. Mustafa, Pore surface fractal analysis of palladium–alumina ceramic membrane using Frenkel–Halsey–Hill (FHH) model, J. Colloid Interface Sci. 301 (2006) 575–584.
[11] B. Venkatesh, D.L. Chen, S.D. Bhole, Three-dimensional fractal analysis of fracture surfaces in a titanium alloy for biomedical applications, Scripta Mater. 59 (2008) 391–394.
[12] H.P. Tang, J.L. Zhu, Z.P. Xi, X.B. Di, J.Y. Wang, Q.B. Ao, Impact factors of fractal analysis of porous structure, Science in China Series E: Technological Sciences 53 (2010) 348–351.
[13] S.N. Kulkov, J. Tomas, S.P. Buyakova, Fractal dimension of the surface of porous ceramic materials, Technical Physics Letters 32 (2006) 73–75.
[14] S. Xie, Q. Cheng, Q. Ling, B. Li, Z. Bao, P. Fan, Fractal and multifractal analysis of carbonate pore-scale digital images of petroleum reservoirs, Mar. Petrol. Geol. 27 (2010) 476–485.
[15] H. Xie, H.W. Zhou, Z. Feng, Fractal property of spatial distribution of acoustic emissions during the failure process of bedded rock salt, Int. J. Rock Mech. Min. Sci. 48 (2011) 1344–1351.
[16] I. Dlouhý, B. Strnadel, The effect of crack propagation mechanism on the fractal dimension of fracture surfaces in steels, Eng. Fract. Mech. 75 (2008) 726–738.
[17] M. Babič, M. Milfelner, S. Stepišnik, Laser hardening metals. In: Perme, T., Švetak, D., Balič, J. (eds.), IRT Industrial Forum, Portorož, 7–8 June 2010. Source of knowledge and experience for the profession: Proceedings of the Forum. Skofljica: Profidtp.
[18] Stoev, S. Pipiras, V., and Taqqu, M. S. (2002). Estimation of the self-similarity parameter in linear fractional stable motion. Signal Processing 82, 1873-1901.
[19] BABIČ, Matej, KOKOL, Peter, GUID, Nikola, PANJAN, Peter. A new method for estimating the Hurst exponent H for 3D objects. Materiali in tehnologije, ISSN 1580-2949, (2014), 48 (2) 203-208.
[20] J. R. Koza. Course Notes for Genetic Algorithms and Genetic Programming. Spring, (2002).
[21] Vadlamani Ravi, Nekuri Naveen, Mayank Pandey. Hybrid classification and regression models via particle swarm optimization auto associative neural network based nonlinear PCA. International Journal of Hybrid Intelligent Systems. 10 (3), (2013).

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