Modeling and Analysis of the Plastic Flow Curves of a Duplex Stainless Steel Using Artificial Intelligence

Abstract

Determining the flow stress curves of metals and alloys in hot working conditions is essential for designers of metals forming processes. In this research, samples of a super duplex stainless steel with a ferrite matrix and dispersed austenite particles were deformed by torsion tests at temperatures ranging from 900 °C to 1200 °C and strain rates from 0.01 s^-1 to 10 s^-1. The level and shape of the plastic flow stress curves depend on the temperature and the strain rate and varies with the austenite volume fraction. When the two phases are deformed together, the marked difference in the softening behavior of austenite and ferrite leads to the uneven strain partitioning between these phases. As a consequence, the plastic behavior of this biphasic material is more complex than that of a single-phase material. A four columns spreadsheet was built using the experimental data obtained from the hot deformation testing. The first three columns contain the input data attributes (temperature, strain rate and strain) and the fourth the strength (stress) resulting from the material during deformation. These data were submitted to machine learning algorithms; initially in an artificial neural network with one hidden layer (ANN) and subsequently to a neural network with a specialist system (ANFIS). After the machine learning processes, the plastic flow curves were rebuilt and compared with those obtained experimentally. The ability of both algorithms to rebuilt the plastic flow curves of the super duplex stainless steel were associated with changes in the shapes of the flow curves and microstructure evolution.

Keywords:
Duplex Stainless Steel; Modeling Flow curves; Artificial Intelligence; Machine learning