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Faramarz Fereshteh-Saniee,S. Hassan Nourbakhsh,S. Mahmoud Pezeshki 대한기계학회 2012 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.26 No.1
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.
Hossein Khazaali,Faramarz Fereshteh-Saniee 한국정밀공학회 2019 International Journal of Precision Engineering and Vol.20 No.5
In the present investigation, the effects of the initial temperature of the sheet, tool diameter and vertical pitch on the final temperature, drawing depth, forming limit diagrams and thickness reduction, resulted from warm incremental forming of titanium sheets are investigated using the Taguchi method. The geometry of a truncated cone with varying wall angle was used for further reduction in the necessary experiments, materials and time. It was shown that any growth in the initial temperature of the sheet or vertical pitch would enhance the formability of the sheet due to the metal softening. The vertical pitch was also the main factor in increasing the final temperature, whereas the tool diameter was the most effective parameter for improving the drawing depth, the average of the major strains and thickness reduction. Moreover, the Sine law was applicable for estimation of final thickness of the incrementally deformed sheets.