Fengyi Yu

A thermal–metallurgical model of laser beam welding simulation for carbon steels

A coupled thermal–metallurgical model is developed to predict the temperature fields and spatial distribution of volume fraction of phases during laser beam welding of 1020, 1045, and 1060 steels. The classical transient heat conduction model is used to calculate the temperature fields during laser beam welding. For phase transformation, the austenization, the austenite-to-pearlite/ferrite transformation, the austenite-to-bainite transformation, and the austenite-to-martensite transformation are modeled respectively. All of these transformation models are solved by the finite element method (FEM) based on the simulated temperature fields. The thermal properties of the three steels are determined by the linear interpolation base of the phase fractions, and thermal properties for each pure phase. The temperature fields and spatial distribution of phases are predicted by 3D finite element method (FEM) code which is developed by the authors to solve the thermal–metallurgical models. In addition, comparison between the coupled model and the pure conduction model without considering phase transformations is carried out to study the influence of phase transformation on temperature fields during welding. According to the comparison, the temperature of the coupled model is higher than the pure conduction model in the temperature region above 1000 °C, but the temperature profiles are very similar at the temperature region under 1000 °C. The predicted volume fractions of 1020 and 1060 steels are close to experimental results. However, there is an obvious difference between predicted and experimental results of the phase fraction of 1045 steels.

A phase field investigation of dendrite morphology and solute distributions under transient conditions in an Al-Cu welding molten pool

A quantitative phase field model is developed to simulate dendrite morphology and solute distributions of the Al–4 wt-% Cu alloy in Gas Tungsten Arc Welding (GTAW) welding molten pool under transient conditions. The functions of temperature gradient and travel velocity are used to obtain transient conditions of the welding molten pool. Time evolutions of the dendrite morphology, solute distributions of different positions and interfaces are obtained. The dendrite growth process can be divided into four stages, namely linear growth, non-linear growth, competitive growth and short-term steady growth. The solute concentration near the primary dendrite tip region is the smallest, while solute concentration is larger in the front of plane crystals growth interface and the solute concentration in the liquid region among the primary dendrites is obviously the largest, where the solute segregation forms readily. For the given welding parameters, the dendrite morphology and the initial instability of solid/liquid interface agree well with the experimental result.

Cellular Automaton Study of Hydrogen Porosity Evolution Coupled with Dendrite Growth During Solidification in the Molten Pool of Al-Cu Alloys

Welding porosity defects significantly reduce the mechanical properties of welded joints. In this paper, the hydrogen porosity evolution coupled with dendrite growth during solidification in the molten pool of Al-4.0 wt pct Cu alloy was modeled and simulated. Three phases, including a liquid phase, a solid phase, and a gas phase, were considered in this model. The growth of dendrites and hydrogen gas pores was reproduced using a cellular automaton (CA) approach. The diffusion of solute and hydrogen was calculated using the finite difference method (FDM). Columnar and equiaxed dendrite growth with porosity evolution were simulated. Competitive growth between different dendrites and porosities was observed. Dendrite morphology was influenced by porosity formation near dendrites. After solidification, when the porosities were surrounded by dendrites, they could not escape from the liquid, and they made pores that existed in the welded joints. With the increase in the cooling rate, the average diameter of porosities decreased, and the average number of porosities increased. The average diameter of porosities and the number of porosities in the simulation results had the same trend as the experimental results.

Simulation of primary dendrite arm spacing in an Al–Cu welding molten pool

Primary dendrite arm spacing in tungsten inert gas welding pool for Al–4wt%Cu alloys is predicted by a quantitative phase-field model. Transient conditions are obtained by functions of thermal gradient and solidification rate. When the same welding power is given by 3500 W and different welding velocities are given by 1.5, 2.0 and 2.5 mm s−1, the primary dendrite arm spacing obtained by simulation results is the largest under the welding velocity of 2.0 mm s−1. When the same welding velocity is given by 2.5 mm s−1 and different welding power is given by 3500, 4000 and 5000 W, the primary dendrite arm spacing acquired by simulation results is the largest under the welding power of 5000 W. Primary dendrite arm spacing and morphology obtained by simulation results agrees well with experimental findings.

Effect of Surface Tension Anisotropy and Welding Parameters on Initial Instability Dynamics During Solidification: A Phase-Field Study

The effects of surface tension anisotropy and welding parameters on initial instability dynamics during gas tungsten arc welding of an Al-alloy are investigated by a quantitative phase-field model. The results show that the surface tension anisotropy and welding parameters affect the initial instability dynamics in different ways during welding. The surface tension anisotropy does not influence the solute diffusion process but does affect the stability of the solid/liquid interface during solidification. The welding parameters affect the initial instability dynamics by varying the growth rate and thermal gradient. The incubation time decreases, and the initial wavelength remains stable as the welding speed increases. When welding power increases, the incubation time increases and the initial wavelength slightly increases. Experiments were performed for the same set of welding parameters used in modeling, and the results of the experiments and simulations were in good agreement.

Effect of Temperature and Fluid Flow on Dendrite Growth During Solidification of Al-3 Wt Pct Cu Alloy by the Two-Dimensional Cellular Automaton Method

A two-dimensional cellular automaton–finite volume model was developed to simulate dendrite growth of Al-3 wt pct Cu alloy during solidification to investigate the effect of temperature and fluid flow on dendrite morphology, solute concentration distribution, and dendrite growth velocity. Different calculation conditions that may influence the results of the simulation, including temperature and flow, were considered. The model was also employed to study the effect of different undercoolings, applied temperature fields, and forced flow velocities on solute segregation and dendrite growth. The initial temperature and fluid flow have a significant impact on the dendrite morphologies and solute profiles during solidification. The release of energy is operated with solidification and results in the increase of temperature. A larger undercooling leads to larger solute concentration near the solid/liquid interface and solute concentration gradient at the same time-step. Solute concentration in the solid region tends to increase with the increase of undercooling. Four vortexes appear under the condition when natural flow exists: the two on the right of the dendrite rotate clockwise, and those on the left of the dendrite rotate counterclockwise. With the increase of forced flow velocity, the rejected solute in the upstream region becomes easier to be washed away and enriched in the downstream region, resulting in acceleration of the growth of the dendrite in the upstream and inhibiting the downstream dendrite growth. The dendrite perpendicular to fluid flow shows a coarser morphology in the upstream region than that of the downstream. Almost no secondary dendrite appears during the calculation process.