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Math. Comput. Appl. 1997, 2(3), 119-125; doi:10.3390/mca2030119

An Investigation of Deep Drawing of Low Carbon Steel Sheets and Applications in Artificial Neural Networks

Celal Bayar University, Engineering Faculty, 45140 Manisa, Turkey
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Published: 1 December 1997
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Abstract

In this study, the deep drawability of SAE 6114, being a low carbon steel, was investigated. The materials with thickness varying from 0.67 mm to 2 mm were subjected to tensile tests and then R (average vertical anisotropy coefficient) and n (stain hardening exponent) values were determined. At the same time, h (the height of the cup) and F (the reaction force) values of the materials were found by subjecting them to Erichsen test A sheet with 2 mm thickness was cold rolled in 6 different deformation ratios and the tests were applied to it Results obtained from the tests were compared with each other and ANN application was performed for these results.
It was proved that, there was an ANN solution to obtain new values of % deformation rate and thickness properties of deep drawing of low carbon steel sheets which were found by experiment The obtained values satisfied our estimation.
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Meriç, C.; Köksal, N.S.; Karlık, B. An Investigation of Deep Drawing of Low Carbon Steel Sheets and Applications in Artificial Neural Networks. Math. Comput. Appl. 1997, 2, 119-125.

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Math. Comput. Appl. EISSN 2297-8747 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
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