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Estimation of flow curve and friction coefficient by means of a one-step ring test using a neural network coupled with FE simulations
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  • Estimation of flow curve and friction coefficient by means of a one-step ring test using a neural network coupled with FE simulations
  • Estimation of flow curve and friction coefficient by means of a one-step ring test using a neural network coupled with FE simulations
저자명
Fereshteh-Saniee. Faramarz,Nourbakhsh. S. Hassan,Pezeshki. S. Mahmoud
간행물명
Journal of mechanical science and technology
권/호정보
2012년|26권 1호|pp.153-160 (8 pages)
발행정보
대한기계학회
파일정보
정기간행물|ENG|
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이 논문은 한국과학기술정보연구원과 논문 연계를 통해 무료로 제공되는 원문입니다.
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기타언어초록

This paper is concerned with application of artificial neural network (ANN) to the ring compression test for simultaneous determination of the flow curve of the material and the friction factor. The developed ANN model was trained using data from 700 finiteelement (FE) simulations of the ring test. The load curve of this test and the final internal diameter of the sample are the inputs for this ANN model and the outputs are the strength coefficient, strain hardening exponent and the friction factor. It was found that the outputs of the developed ANN model were in good agreement with the experimental results.