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1.
Fatigue crack growth and fracture mechanics analysis of a working roll surface layer material
Matej Drobne, Tomaž Vuherer, Ivan Samardžić, Srečko Glodež, 2014, izvirni znanstveni članek

Opis: Fatigue crack growth and fracture mechanics analysis of a working roll surface layer material is presented in this paper. The research is done on a hot strip mill working roll where High Chromium Steel is used for roll’s shell material. To obtain corresponding parameters, a rectangular single edge notched bend specimens – SENB, according to standard BS 7448, were used. The fatigue crack growth analysis was done on a resonant testing machine with use of special crack gauges, while for fracture mechanics parameters the electro–mechanical testing machine was used.
Ključne besede: fracture mechanics, fatigue crack growth, metal forming, rolling process, high chromium steel
Objavljeno v DKUM: 03.07.2017; Ogledov: 1310; Prenosov: 130
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2.
Modeling of forming efficiency using genetic programming
Miran Brezočnik, Jože Balič, Zlatko Kampuš, 2001, izvirni znanstveni članek

Opis: This paper proposes new approach for modeling of various processes in metal-forming industry. As an example, we demonstrate the use of genetic programming (GP) for modeling of forming efficiency. The forming efficiency is a basis for determination of yield stress which is the fundamental characteristic of metallic materials. Several different genetically evolved models for forming efficiency on the basis of experimental data for learning were discovered. The obtained models (equations) differ in size, shape, complexity and precision of solutions. In one run out of many runs of our GP system the well-known equation of Siebel was obtained. This fact leads us to opinion that GP is a very powerful evolutionary optimization method appropriate not only for modeling of forming efficiency but also for modeling of many other processes in metal-forming industry.
Ključne besede: metal forming, yield stress, forming efficiency, mathematical modeling, adaptation, genetic methods, genetic algorithm, genetic programming, artificial intelligence, process optimisation
Objavljeno v DKUM: 01.06.2012; Ogledov: 2212; Prenosov: 123
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