Intelligent system for prediction of mechanical properties of material based on metallographic imagesMatej Paulič
, David Močnik
, Mirko Ficko
, Jože Balič
, Tomaž Irgolič
, Simon Klančnik
, 2015, original scientific article
Abstract: This article presents developed intelligent system for prediction of mechanical properties of material based on metallographic images. The system is composed of two modules. The first module of the system is an algorithm for features extraction from metallographic images. The first algorithm reads metallographic image, which was obtained by microscope, followed by image features extraction with developed algorithm and in the end algorithm calculates proportions of the material microstructure. In this research we need to determine proportions of graphite, ferrite and ausferrite from metallographic images as accurately as possible. The second module of the developed system is a system for prediction of mechanical properties of material. Prediction of mechanical properties of material was performed by feed-forward artificial neural network. As inputs into artificial neural network calculated proportions of graphite, ferrite and ausferrite were used, as targets for training mechanical properties of material were used. Training of artificial neural network was performed on quite small database, but with parameters changing we succeeded. Artificial neural network learned to such extent that the error was acceptable. With the oriented neural network we successfully predicted mechanical properties for excluded sample.
Keywords: artificial neural network, factor of phase coherence between the surfaces, fracture toughness, image processing, mechanical properties, metallographic image, ultimate tensile strength, yield strength
Published: 12.07.2017; Views: 693; Downloads: 340
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Strength mismatch effect on yield load in X-shaped weldment with centre crackDražan Kozak
, Nenad Gubeljak
, Jožef Predan
, Franjo Matejiček
, 2004, published scientific conference contribution
Abstract: This paper provides yield load solutions for single edged fracture toughness specimen subjected to bending SE (B) with present X-shaped weld joint. The weld centre crack is located in the overmatch weld part. The corresponding fully plastic yield loads were obtained directly by plain strain FEM analysis for five characteristic a/W ratios: 0,1;0,2;0,3;0,4 and 0,5. Also, the influence of the sistematically varied weld root width 2H on the fracture behaviour has been evaluated. It was found that yield load decreases with the increasing weld root, because the undermatched region ahead the crack tip plays the dominant role.
Keywords: fracture mechanics, X-shaped welded joints, centre crack, strength mismatching, plastic yield load, constraint effects
Published: 01.06.2012; Views: 1208; Downloads: 30
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Yield strength modelling of formed material using evolutionary computational methodLeo Gusel
, Rebeka Rudolf
, 2009, original scientific article
Abstract: In this paper we propose an evolutionary computation approach for the modelling of yield strength in formed material. One of the most general evolutionary computation methods is genetic programming, which was used in our research. Genetic programming is an automated method for creating a working computer program from a problemćs high-level statement. Genetic programming does this by genetically breeding a population of computer programs using the principles of Darwinianćs natural selection and biologically inspired operations. During our research, material was cold formed by drawing using different process parameters and then determining yield strengths (dependent variable) of the specimens. On the basis of a training data set, various different genetic models for yield strength distribution were developed during simulated evolution. The accuracies of the best models were proved by a testing data set and comparing between the genetic and regression models. The research showed that very accurate genetic models can be developed by the proposed approach.
Keywords: metal forming, yield strength, genetic programming, modelling
Published: 31.05.2012; Views: 1620; Downloads: 27
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