Artificial Neural Networks Used for Development Prediction of State-of-the-Art Surface Engineering Areas

Anna Danuta Dobrzańska-Danikiewicz; Jacek Trzaska; Agnieszka Sękala; Adam Jagiełło

doi:10.4028/www.scientific.net/SSP.220-221.785

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HomeSolid State PhenomenaSolid State Phenomena Vols. 220-221Artificial Neural Networks Used for Development...

Artificial Neural Networks Used for Development Prediction of State-of-the-Art Surface Engineering Areas

Abstract:

The paper presents new, possible applications of artificial neural networks in the field of materials science and material engineering in relation to other artificial intelligence methods known and applied in this area. The most recent simulation experiments, the exemplary results of which are presented in this paper, point out that the scope of the existing applications of artificial neural networks can be extended to encompass new areas related to prediction of development of materials treatment and processing technologies. The goal of such research is to focus, intentionally, the areas of future research and investments on the most promising areas likely to yield the highest added value in the future together with mitigating a risk relating to such a process. The computational models created were used for creating multi-variant probabilistic scenarios of future events based on heuristic independent variables acquired in the process of multi-stage expert surveys. Dependencies were determined, in particular, between the probability of occurrence of alternative macro-scenarios of future events and the development of the relevant thematic areas of M1–M7 and P1–P7.

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Periodical:

Solid State Phenomena (Volumes 220-221)

Pages:

785-789

DOI:

https://doi.org/10.4028/www.scientific.net/SSP.220-221.785

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Online since:

January 2015

Authors:

Anna Danuta Dobrzańska-Danikiewicz*, Jacek Trzaska, Agnieszka Sękala, Adam Jagiełło

Keywords:

Development Perspectives, Neural Network (NN), Simulation Results, Surface Engineering

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References

[1] L.A. Dobrzański, J. Trzaska, Application of neural network for the prediction of continuous cooling transformation diagrams, Computational Materials Science 30(3–4) (2014) 251–259.

DOI: 10.1016/j.commatsci.2004.02.011

Google Scholar

[2] L.A. Dobrzański, T. Tański, J. Trzaska, Modeling of the optimum heat treatment conditions of Mg-Al-Zn magnesium cast alloys, International Journal Computational Materials Science and Surface Engineering 1(5) (2007) 540–554.

DOI: 10.4028/www.scientific.net/msf.638-642.1488

Google Scholar

[3] A.D. Dobrzańska-Danikiewicz, T. Tański, J. Domagała-Dubiel, Unique properties, development perspectives and expected applications of laser treated casting magnesium alloys, Archives of Civil and Mechanical Engineering 12/3 (2012) 318–326.

DOI: 10.1016/j.acme.2012.06.007

Google Scholar

[4] A.D. Dobrzańska-Danikiewicz, Computer integrated development prediction methodology in materials surface engineering, Open Access Library 1(7) (2012) (in Polish).

Google Scholar

[5] A.D. Dobrzańska-Danikiewicz, J. Trzaska, A. Jagiełło, E. Jonda, K. Labisz, Neural networks aided future events scenarios presented on the example of laser surface treatment, Archives of Materials Science and Engineering 51(2) (2011) 69–96.

Google Scholar