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

Shanghua Cheng,Zhujing Shao,Lidong Li,Shaojie Wu,Fangjie Cheng,Yiqi Zhang,Fangliang Li

doi:10.3390/met11091429

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.

Highlights

Liquefied natural gas (LNG) which is the most promising clean energy, has received a lot of attention, as the world grows more concerned with environmental pollution
Since 25 groups of tests with different welding parameters were conducted and 5 ferrite number (FN) values were recorded in each group of tests, a total of 125 samples were obtained for Dongfang Special Steel hot-rolled sheet (DFSS) or ASIS, collectively
The two models were trained by the Levenberg–Marquardt algorithm, which combines the advantages of the Gauss–Newton algorithm and the gradient descent method and can provide numerical solutions of nonlinear minimization

Summary

Introduction

Liquefied natural gas (LNG) which is the most promising clean energy, has received a lot of attention, as the world grows more concerned with environmental pollution. Austenitic stainless steel, especially SS304/304L, characteristized by excellent strength, ductility and good corrosion resistance, is widely used in the LNG corridor [1]. The outstanding mechanical properties of austenitic stainless steel obtain from a certain amount of retained ferrite in its microstructure. The content and morphology of ferrite directly affect the properties of austenitic stainless steel. Hauser and Van Echo [4] found that the strength of austenitic stainless increases with the increase of the ferrite number (FN) from 2 to 16 in the cladding metal, at room temperature. Excessive retained ferrite, which is its body-centered cubic brittle phase, results in the decrease of ductility. As Lippold et al [5] showed, there was a 50% reduction in fracture toughness when the FN increased from zero to ten

Methods

Results

Conclusion