Relationship Analysis between Multi-Parameters and Ferrite Number in GTAW Based on ANN Model

Abstract

Ferrite number (FN) is a crucial parameter for austenite steel-welding products, since it has a specific relationship with crack sensitivity and other important properties. In this paper, artificial neural network (ANN) models were built to predict FN, based on the GTAW tests of 304L plates produced by two different steelworks, Dongfang Special Steel hot-rolled sheet (DFSS) and Anshan Iron and Steel cold-rolled sheet (ASIS). The results show that a high performance, of more than 98% accuracy, can be achieved when the models of DFSS and ASIS are modeled separately, and that accuracy is also above 96% when an integrated model is built. The influences of nitrogen content and multiwelding parameters, such as travel speed, wire-feed rate, welding current and arc length, on FN are also analyzed through the FN-prediction model for DFSS. The results show that FN increases monotonously with the increase of nitrogen content, but the influences of either of the other two parameters on FN are nonlinear.