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.
Highlights
In modern engineering modern methods to solve different problems are nowadays more and more appearing and used
We briefly introduce the whole system, first the image feature extraction algorithm is described and the system for prediction of mechanical properties based on obtained information from metallographic image is described
Prediction of mechanical properties was performed by feed-forward neural network
Summary
In modern engineering modern methods to solve different problems are nowadays more and more appearing and used. Malinov et al [8] developed a model for analysis and prediction of the correlation between processing parameters and mechanical properties in titanium alloys by applying artificial neural network. They used as inputs in artificial neural network the data of alloy composition, heat treatment parameters and work (test) temperature They are predicting nine most important mechanical properties as follows: ultimate tensile strength, tensile yield strength, elongation, reduction of area, impact strength, hardness, modulus of elasticity, fatigue strength and fracture toughness. They present the method for predicting the yield point and the ultimate tensile strength for steel They used artificial neural network for development of models for prediction of these mechanical properties. Bahrami et al [11] researched the use of artificial neural networks for prediction of mechanical properties based on different morphology and volume fractions of martensite
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