Application of Artificial Neural Networks to Predict the Carbon Content and the Grain Size for Carbon Steels | ||||
| Egyptian Journal of Solids | ||||
| Article 7, Volume 25, Issue 2, 2002, Page 229-243 PDF (244.88 K) | ||||
| DOI: 10.21608/ejs.2002.150480 | ||||
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| Abstract | ||||
| Neural networks and generic algorithms are two branches of artificial intelligence that can provide many benefits in engineering applications. The artificial neural networks (ANN) technologies provide on-line capability to analyze many inputs and provide information to multiple outputs, and also, have the capability to learn or adapt to changing conditions. No doubt that the determination of either of the carbon content or the grain size of carbon steel is a time consuming process; which involves a quite tedious work. This paper examines the feasibility of using an integration system between some measured ultrasound parameters; from nondestructive test (NDT), and a pre - learned ANN to facilitate the determination of grain size and carbon content for the tested samples. The results showed that grain size and carbon content of carbon steels can be well predicted using a trained neural networks, with an acceptable degree of errors and great reliability. | ||||
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