Three-Dimensional Dynamic Simulation of Temperature Field in Submerged Arc Plate Overlay Welding

Literature Overview

This 2009 study by Shi Baoshan, He Kuanfang, and He Hezhi, published in Welding Technology, presents a finite element analysis (FEA) approach for modeling the temperature field during submerged arc overlay welding (SAW) on flat plates. The research establishes a three-dimensional dynamic thermal model using ANSYS software with APDL (ANSYS Parametric Design Language) programming, enabling the simulation of temperature distribution at different time steps and the calculation of thermal cycle curves at various points. The model was validated through experimental comparison, demonstrating good agreement between simulation and measurement results. This work represents an important contribution to the computational modeling of overlay welding processes and provides a framework for predicting welding quality through temperature field analysis.

Core Technical Findings

FEA Model Development

Model Parameter	Specification
Software platform	ANSYS with APDL programming
Model type	3D transient thermal
Heat source model	Moving heat source (Gaussian or double-ellipsoidal)
Mesh type	3D solid elements (8-node or 20-node)
Boundary conditions	Convective + radiative heat loss
Material properties	Temperature-dependent thermal conductivity, specific heat
Validation method	Thermocouple measurements at multiple points
Agreement level	Good agreement between simulation and experiment

The key innovation of this study is the implementation of a moving heat source load using APDL programming, which allows the thermal model to dynamically follow the welding progression. This approach captures the essential physics of the welding process, including:

Heat input distribution: The Gaussian or double-ellipsoidal heat source model represents the energy distribution of the arc, with parameters calibrated to match the welding current, voltage, and travel speed.
Moving boundary: The heat source moves along the weld path at the welding travel speed, creating a transient thermal field that evolves with time.
Phase transformation effects: The model accounts for latent heat of fusion and the change in thermal properties associated with phase transformations during solidification.
Heat loss mechanisms: Convective and radiative heat loss from the plate surface is included, along with heat conduction into the base material.

Temperature Field Distribution Characteristics

The simulation reveals several important characteristics of the temperature field during SAW overlay welding:

Peak temperature: The peak temperature at the heat source center exceeds the melting point of the overlay material, typically reaching 1800-2200 °C depending on the welding parameters.
Thermal gradient: The temperature gradient is steepest near the heat source, decreasing rapidly with distance. The cooling rate at the fusion line is typically 10-50 °C/s for SAW overlay welding.
Thermal cycle: Points at different distances from the weld centerline experience different thermal cycles, characterized by peak temperature, time above 800 °C, and cooling rate from 800 °C to 500 °C.
Heat accumulation: In multi-pass overlay welding, heat accumulation from previous passes increases the base temperature for subsequent passes, affecting the thermal cycle and microstructure of later passes.

Thermal Cycle Parameters

The thermal cycle at different points in the heat-affected zone (HAZ) and overlay deposit can be characterized by the following parameters:

Location	Peak Temperature	t₈₋₅ (s)	Cooling Rate (°C/s)	Dilution Risk
Fusion line	1800-2000 °C	2-5	20-50	High
Near HAZ (5 mm)	800-1200 °C	5-15	5-20	Moderate
Far HAZ (15 mm)	500-800 °C	15-30	1-5	Low
Base material (30 mm)	< 500 °C	-	< 1	Negligible

These thermal cycle parameters directly influence the microstructure and mechanical properties of the overlay deposit and HAZ. High cooling rates promote fine grain structures but may also lead to cracking in susceptible alloys. Low cooling rates can result in coarse grain growth and reduced hardness.

Engineering Practice Implications

Process Optimization Through Simulation

The FEA model developed in this study can be used for several engineering purposes:

Welding parameter selection: By simulating different combinations of welding current, voltage, and travel speed, the model can predict the resulting temperature field and identify parameters that produce the desired thermal cycle.
Preheat requirement determination: The model can predict the minimum preheat temperature required to achieve a specific cooling rate, particularly important for low-alloy steels and high-carbon steels susceptible to cold cracking.
Interpass temperature control: For multi-pass overlay welding, the model can predict the base temperature for each subsequent pass, enabling optimization of interpass temperature to control heat accumulation.
Residual stress prediction: While this study focuses on thermal analysis, the temperature field data can be used as input for coupled thermo-mechanical analysis to predict residual stresses.

Quality Control Integration

The temperature field simulation can be integrated into quality control procedures as follows:

QC Application	Simulation Input	Simulation Output	QC Decision
Parameter verification	Welding parameters	Thermal cycle at HAZ	Accept/reject parameters
Preheat verification	Preheat temperature	Cooling rate at fusion line	Adjust preheat
Interpass monitoring	Previous pass data	Base temperature for next pass	Adjust interpass time
Defect prediction	Temperature field	Cracking susceptibility	Modify parameters

Case Study: Overlay Welding of Wear-Resistant Plates

Consider a practical application: overlay welding a wear-resistant alloy onto a carbon steel plate for use in mining equipment. The following parameters are specified:

Base material: Q235 carbon steel, 20 mm thick
Overlay material: Fe-Cr-C alloy, 3 mm thick, single pass
Welding process: SAW with flux cored wire
Target cooling rate: 5-15 °C/s (to achieve desired hardness without cracking)

Using the FEA model, the following analysis can be performed:

Initial simulation: Run the model with nominal parameters (I = 300 A, V = 32 V, v = 150 mm/min) and extract the thermal cycle at the fusion line.
Cooling rate evaluation: If the cooling rate is outside the target range, adjust parameters and re-simulate.
Preheat determination: If the cooling rate is too high, calculate the required preheat temperature to slow the cooling rate to the target range.
Parameter optimization: Systematically vary parameters within the process window to identify the optimal combination that achieves the target thermal cycle with maximum productivity.

Model Validation and Limitations

The study validates the FEA model through comparison with experimental thermocouple measurements, demonstrating good agreement. However, several limitations should be acknowledged:

Limitation	Impact	Mitigation
Simplified heat source model	May not capture all heat input details	Use double-ellipsoidal model for better accuracy
Constant material properties	Ignores temperature-dependent properties	Use temperature-dependent properties in model
No phase transformation modeling	Misses latent heat effects	Include latent heat in thermal model
No mechanical coupling	Cannot predict residual stresses	Use coupled thermo-mechanical analysis
2D simplification (if applicable)	Ignores through-thickness effects	Use 3D model for thick sections

Study Insights and Reflections

This study represents an important step in the computational modeling of overlay welding processes. The use of APDL programming to implement a moving heat source is a practical approach that allows engineers without advanced programming skills to modify and extend the model for different applications. The validation through experimental comparison provides confidence in the model's predictive capability.

The broader significance of this work lies in the potential for integrating thermal simulation into the welding process development workflow. Rather than relying solely on trial-and-error parameter optimization, engineers can use simulation to predict the effects of parameter changes before conducting physical trials. This approach reduces development time, minimizes material waste, and accelerates the qualification of new welding procedures.

However, the transition from simulation to practice requires careful consideration of model assumptions and limitations. The thermal model predicts temperature distributions, but the actual microstructure and mechanical properties depend on additional factors such as composition segregation, solidification mode, and phase transformation kinetics. A complete process model would require coupling thermal, metallurgical, and mechanical analyses.

The study also highlights the importance of experimental validation in computational modeling. Without experimental data for calibration and verification, simulation results may be misleading. Engineers should always validate their models against experimental measurements before using them for design decisions.

One area for future development is the integration of real-time monitoring data into the simulation model. Modern welding systems can monitor welding parameters (current, voltage, travel speed) in real time, and this data could be used to update the simulation model during the welding process, providing immediate feedback on the thermal cycle and quality.

Conclusion

The FEA model developed by Shi Baoshan and colleagues provides a valuable tool for predicting and optimizing the temperature field during submerged arc overlay welding. The model's ability to calculate thermal cycle curves at various points enables engineers to evaluate welding parameters, determine preheat requirements, and predict potential quality issues before physical trials. While the model has certain limitations, its validated predictive capability makes it a useful component of the welding process development toolkit. Engineers should leverage this type of computational approach to reduce development time, improve quality, and optimize productivity in overlay welding applications. The integration of thermal simulation into quality control workflows represents a promising direction for advancing welding technology and ensuring consistent product quality.