Technique of Metals Strength Properties Diagnostics Based on the Complex Use of Fuzzy Inference System and Hybrid Neural Network

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13440%2F20%3A43895693" target="_blank" >RIV/44555601:13440/20:43895693 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/book/10.1007/978-3-030-61656-4" target="_blank" >https://link.springer.com/book/10.1007/978-3-030-61656-4</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Technique of Metals Strength Properties Diagnostics Based on the Complex Use of Fuzzy Inference System and Hybrid Neural Network

Popis výsledku v původním jazyce

The results of the research concerning development of the technique of metals strength properties diagnostics using combination of the methods of non-destructive control based on the complex use of fuzzy inference system and hybrid neural network are presented in the paper. The acoustic non-destructive control method, the electromagnetic method and hardness control were used as the control methods within the framework of the proposed technique. The selection of the optimal combination of the methods was performed using fuzzy inference system, in which, the final solution was taken applying Harrington desirability function. The metal strength properties were determined using hybrid neural network the basis of which are fuzzy neurons. The simulation results with the use of samples of Y8 steel have shown that the combination of acoustic and electromegnetic methods of non-destructive testing is an optimal in terms of maximum value of heneral Harrington desiribility index and the hybrid neural network with two layers of neurons and triangular membership functions with combine algorithm of network training is an optimal one in terms of relative error of metals strength properties evaluation. To our mind, the proposed technique may allow us to increase the exactness of metals strength properties determination when the non-destructive methods of control are applied.

Název v anglickém jazyce

Technique of Metals Strength Properties Diagnostics Based on the Complex Use of Fuzzy Inference System and Hybrid Neural Network

Popis výsledku anglicky

The results of the research concerning development of the technique of metals strength properties diagnostics using combination of the methods of non-destructive control based on the complex use of fuzzy inference system and hybrid neural network are presented in the paper. The acoustic non-destructive control method, the electromagnetic method and hardness control were used as the control methods within the framework of the proposed technique. The selection of the optimal combination of the methods was performed using fuzzy inference system, in which, the final solution was taken applying Harrington desirability function. The metal strength properties were determined using hybrid neural network the basis of which are fuzzy neurons. The simulation results with the use of samples of Y8 steel have shown that the combination of acoustic and electromegnetic methods of non-destructive testing is an optimal in terms of maximum value of heneral Harrington desiribility index and the hybrid neural network with two layers of neurons and triangular membership functions with combine algorithm of network training is an optimal one in terms of relative error of metals strength properties evaluation. To our mind, the proposed technique may allow us to increase the exactness of metals strength properties determination when the non-destructive methods of control are applied.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Communications in Computer and Information Science

ISBN

978-3-030-61655-7

ISSN

1865-0929

e-ISSN

1865-0937

Počet stran výsledku

13

Strana od-do

114-126

Název nakladatele

Springer Nature Switzerland AG

Místo vydání

Switzerland

Místo konání akce

Lviv, Ukraine

Datum konání akce

21. 8. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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