الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
In the present investigation, the impact energy of the AISI 304L austenitic
stainless steel (ASS) alloy welded joints were evaluated at both ambient and low temperatures up to -80 oC. Moreover, regression analysis (RA) and artificial neural networks (ANN) techniques were used to develop models to predict the impact energy of the AISI 304L base alloy (BA) and the welded joints. A comparison between the two developed RA and ANN models was carried out.The results revealed that the impact energy of the AISI 304L base alloy and
welded joints was found to be reduced with reducing the test temperature. However,the AISI 304L base alloy exhibited higher impact energy when compared with the welded joints. Such behavior was observed at both ambient and low temperatures.Both RA and ANN models can be used to predict the impact energy of the AISI 304L base alloy as well as the welded AISI 304L joints as function of the temperature at both ambient and low temperatures. The RA and ANN models exhibited mean relative errors (MRE) of 1.22% and 1.34% for the AISI 304L base alloy, respectively. While the RA and ANN models exhibited MRE of 2.13% and 1.41% for the welded AISI 304L joints, respectively.