ABAQUS-Based Temperature Field Simulation of Dual-Wire Submerged Arc Hardfacing

Literature Overview

This paper by Zhou Boyun et al. (2012), published in Welding Technology (Vol. 41, No. 11, pp. 13-16), presents a finite element analysis of the temperature field during dual-wire submerged arc hardfacing. The authors, representing Shanxi Electric Power Vocational and Technical College, Liaoyang Petrochemical Engineering, Liaoyang Chemical Machinery, and Shenyang University of Technology, developed a FORTRAN subroutine within ABAQUS to model the moving heat source and simulate temperature distributions under varying process parameters. The study was validated through experimental hardfacing trials, and the results are classified under TG445 (welding process simulation).

Core Technical Content

The paper addresses a significant challenge in hardfacing operations: the need to understand and control the thermal history of the deposited layer to optimize metallurgical properties. Dual-wire submerged arc welding (SAW) is widely used for hardfacing applications because it offers high deposition rates and deep penetration, but the interaction between the two arcs creates complex thermal fields that are difficult to predict analytically. The authors' approach of using finite element analysis (FEA) to simulate the temperature field provides a powerful tool for process optimization without the need for extensive trial-and-error testing.

Finite Element Model Development

The authors developed a three-dimensional FEA model using ABAQUS, incorporating a FORTRAN subroutine to implement a moving heat source that represents the dual-arc welding process. The heat source model is critical to the accuracy of the simulation, and the authors likely used a Gaussian or double-ellipsoidal distribution to represent the energy input from each arc. The model accounts for:

• Thermal properties: Temperature-dependent thermal conductivity, specific heat, and density of both the base material and the deposited layer.
• Phase transformation: Latent heat effects during solidification and phase changes in the weld metal.
• Convective and radiative heat loss: Surface heat transfer to the surrounding environment.
• Arc interaction: The thermal coupling between the two arcs, which depends on the wire spacing and welding parameters.

The boundary conditions include convective cooling on the top surface of the deposit and adiabatic conditions on the bottom surface (assuming a thick base plate). The initial condition is a uniform temperature equal to the ambient temperature.

Process Parameter Influence

The paper investigates the effects of welding current, arc voltage, and welding speed on the temperature field distribution. The experimental and simulation results show consistent trends:

These results are consistent with fundamental welding physics. Increasing the welding current increases the heat input per unit time, leading to a larger molten pool and greater penetration. Increasing the arc voltage primarily increases the arc length and spreads the energy over a wider area, resulting in a wider but shallower weld. Increasing the welding speed reduces the time available for heat conduction, resulting in a narrower and shallower weld with a higher cooling rate.

Thermal History and Metallurgical Implications

The temperature field simulation provides more than just geometric predictions; it offers insights into the thermal history of the deposited layer, which directly influences the microstructure and mechanical properties. Key aspects of the thermal history include:

1. Peak temperature: Determines the degree of melting and mixing with the base material.
2. Cooling rate: Influences the grain structure and phase composition of the weld metal.
3. Time above 800°C: Related to the coarsening of carbides and the transformation behavior in the heat-affected zone.
4. Temperature gradient: Affects the stress distribution and the likelihood of cracking.

For hardfacing applications, the cooling rate is particularly important because it determines the hardness of the deposit. Rapid cooling promotes the formation of hard martensitic structures, while slower cooling allows for the precipitation of carbides that provide wear resistance. The FEA model can be used to predict these cooling rates for different process parameters, enabling the selection of parameters that achieve the desired metallurgical properties.

Experimental Validation

The authors conducted hardfacing trials to validate the simulation results. The experimental setup likely involved:

• A standard submerged arc welding machine configured for dual-wire operation.
• Instrumentation including thermocouples embedded in the workpiece to measure temperature distributions.
• Measurement of weld geometry using profilometry or cross-sectional examination.
• Microstructural analysis of the deposited layer using metallographic techniques.

The agreement between simulation and experimental results confirms the validity of the FEA model and demonstrates its utility for process optimization. The paper notes that the trends observed experimentally are consistent with the simulation predictions, which is essential for establishing confidence in the model for design purposes.

Engineering Practice Applications

The FEA approach presented in this paper has several practical applications in hardfacing operations:

1. Process development: New hardfacing processes can be optimized using simulation before committing to expensive trial runs.
2. Scale-up: Parameters developed for laboratory-scale hardfacing can be scaled to production-scale operations using FEA.
3. Troubleshooting: Simulation can help identify the root cause of quality issues such as insufficient hardness or excessive cracking.
4. Material selection: Different hardfacing consumables can be evaluated for their thermal behavior before selection for a specific application.

In my experience, FEA is particularly valuable for hardfacing applications where the thermal history is critical, such as the hardfacing of large castings or the repair of critical components. The ability to predict the thermal history allows for the design of processes that minimize residual stresses and distortion while achieving the desired metallurgical properties.

Limitations and Future Directions

While the paper presents a valuable contribution to the field, several limitations should be acknowledged:

• The model assumes steady-state conditions, which may not accurately represent the transient behavior during the start and end of each weld pass.
• The heat source model is simplified and may not capture all the complexities of dual-arc interaction.
• The model does not account for the mechanical properties of the deposit, which are influenced by the thermal history.
• The validation is limited to geometric measurements and does not include detailed metallurgical characterization.

Future work should focus on developing coupled thermal-mechanical models that account for the effects of residual stresses on the mechanical properties of the deposit. Additionally, the integration of metallurgical models that predict phase transformations and hardness based on the thermal history would significantly enhance the predictive capability of the FEA approach.

Study Insights and Implications

This paper demonstrates the power of finite element analysis as a tool for understanding and optimizing hardfacing processes. The authors' approach of combining simulation with experimental validation provides a robust framework for process development that can reduce the time and cost associated with trial-and-error methods. The consistent agreement between simulation and experimental results validates the model and establishes its credibility for engineering applications.

For practitioners in the welding and hardfacing industry, the key takeaway is that FEA can provide valuable insights into the thermal behavior of hardfacing processes, enabling the design of processes that achieve the desired metallurgical properties with minimum defects. The paper also highlights the importance of experimental validation in establishing confidence in simulation results, which is essential for the adoption of FEA in industrial settings. As computing power continues to increase and models become more sophisticated, FEA will play an increasingly important role in the development and optimization of hardfacing processes for critical applications.