Dependency of phase transformation on the prior austenite grain size and its influence on welding residual stress of S700 steel

Abstract

The austenite grain size (AGS) before decomposition is a crucial factor for the development of microstructure. However, this dependency is seldom discussed due to the difficulty of observing the grain growth of austenite during welding. In the current work, a grain growth algorithm is combined in a thermodynamics-based metallurgical model for the first time to analyse the influence of prior austenite grain size (pAGS). The phase volume fractions predicted at different cooling rates and pAGSs are compared with the experimental results of the continuous cooling transformation (CCT) diagram. To further investigate the influences of pAGS and microstructure on residual stress, experiments of bead-on-plate welding are conducted at three heat inputs, in which plates of S700 steel are operated by the arc welding process. The geometries after welding, chemical composition in the fusion zone (FZ) and the parameters of the double ellipsoidal heat source are calibrated using the software SimWeld. These geometries are imported to ABAQUS to create a finite element (FE) model. The validated metallurgical model together with the grain growth algorithm are implemented in the subroutine ABAMAIN to provide a thorough prediction of microstructure. With the knowledge of temperature and phase distributions, a coupled thermo-metallomechanical FE model is established to predict the residual stress distributions. The material properties are assigned by interpolating the individual phase property with its volume fraction. By comparing the results predicted by the model assuming constant pAGS, the influence of the pAGS on the residual stress is manifested. Moreover, simulations using overall material properties are also conducted. The stress distributions in the middle of plate surface are plotted along with the volume fractions of product phases to analyse the sensitivity of the residual stress to microstructure.

Numerical prediction of microstructure and hardness for low carbon steel wire Arc additive manufacturing components

“…5, the thermal analysis for WAAM process includes heat source model and heat transfer model, which are similar to welding process. The heat transfer modelling of WAAM process was described in our previous paper [25]. In this work, the metallurgical phase transformation model for WAAM component is studied.…”

“…The metallurgical model for GMAW was implemented through ABAQUS user subroutine AMAMIN developed by Ni et al [25]. After thermal field analysis by using ABAQUS CAE, the temperature history and temperature gradients on every node are read by Python script, and are used to calculate the heating and cooling rates.…”

“…Q aP is the apparent energy required for grain growth. n r denotes the magnitude of the resistance caused by existent precipitates [25]. For low carbon alloy steel, the phase transformation is repeated by several cycles in multi-additive layers.…”

“…Where p, Q a and C g are constants depending upon the chemical composition, and R is gas constant. Table 1 presents the sequence and symbols of phases and structure considered in simulation [25].…”

Probing strain rate effect on the creep–fatigue fracture mechanism of 9%Cr steel‐welded joint via nanoindentation characterization

“…Welding technologies are widely used for manufacture of modern fossil fuel and nuclear power plants. [1][2][3] As a critical part of the welded structure in power plants, welded joints are subjected to complex loadings such as creep, fatigue, and creep-fatigue (CF) interaction during longterm service due to frequent startups and shutdowns at high temperature. [4][5][6] The strain rate is usually random and unstable in the startups and shutdowns process.…”