Three-Dimensional Finite Element Simulation of Temperature Field During TIG Welding of Low-Alloy Steel Thin Plates

Literature Overview

The paper by Guo Yanbing, Tong Yanguang, and He Xiaona (2010), published in Hot Working Technology (Vol. 39, No. 21, pp. 158–160), presents a three-dimensional finite element analysis (FEA) of the temperature field distribution during TIG welding of thin low-alloy steel plates used in automotive transmission clutch discs. The authors employed an equal-density distribution volume heat source model to simulate the thermal input from the TIG arc, and demonstrated that this simplified approach yields reasonably accurate predictions of temperature field distribution and molten pool morphology.

Core Technical Content

Significance of Thermal Simulation in TIG Welding

TIG welding is widely used for precision welding of thin sections where heat input control is critical. Unlike MIG/MAG welding, TIG produces a concentrated, stable arc with minimal spatter, but the narrow heat-affected zone (HAZ) and steep thermal gradients make it susceptible to cracking, distortion, and microstructural changes. Finite element simulation of the temperature field is therefore an essential tool for:

• Predicting the extent of the HAZ and the peak temperatures achieved in the base metal.
• Evaluating the cooling rate (particularly the t₈₀₀₋₆₀₀ parameter) which governs the microstructure and mechanical properties of the weld and HAZ.
• Optimizing welding parameters (current, travel speed, gas flow) to minimize distortion and residual stress.

Heat Source Model

The authors adopted an equal-density distribution volume heat source, which is a simplified representation of the TIG arc energy deposition. This model distributes the welding heat input uniformly within a defined volume element, in contrast to more complex models such as:

• Double-ellipsoidal (Goldak) model: Commonly used for MIG/MAG welding, with separate front and rear heat source distributions.
• Moving point heat source: Simplest approximation, suitable for thin plates with high travel speeds.
• Cylinder heat source: Represents the arc as a moving cylinder of uniform heat flux.

The authors demonstrated that for TIG welding of thin plates, the equal-density volume model provides sufficient accuracy for practical engineering purposes while maintaining computational efficiency. This is an important finding because complex models can require significant computational resources and may not provide proportionally better predictions for thin-section welding.

Simulation Results and Validation

The simulation results showed:

• The temperature field distribution was reasonably symmetric about the weld centerline, consistent with experimental observations.
• The molten pool shape (depth and width) was in good agreement with macrograph observations.
• The peak temperature at the weld surface reached approximately 1800–2200°C, consistent with the melting point of low-alloy steel plus superheat.
• The cooling rate in the HAZ was estimated to be in the range of 10–50°C/s, depending on the distance from the weld centerline.

These results provide a foundation for subsequent analysis of residual stress distribution and welding distortion, which the authors noted as the next logical step in the simulation workflow.

Process and Standards Analysis

Typical TIG Welding Parameters for Thin Low-Alloy Steel

The simulation approach is particularly valuable for thin plates (1–3 mm) where the HAZ is very narrow and small changes in heat input can lead to significant differences in microstructure and residual stress.

Connection to Residual Stress and Distortion Prediction

The temperature field is the primary input for coupled thermo-mechanical FEA. The residual stress field is developed through:

1. Thermal expansion: The heated region expands, while the cooler surrounding material constrains this expansion.
2. Plastic deformation: At high temperatures, the material yields and undergoes plastic flow.
3. Cooling contraction: As the weld cools, the contraction is constrained, leading to tensile residual stresses in the weld and compressive stresses in the surrounding material.

The authors' temperature field model can therefore serve as the first step in a multi-step simulation approach to predict and control welding distortion.

Study Insights and Reflections

This paper represents a practical approach to welding simulation that prioritizes computational efficiency without sacrificing significant accuracy. The choice of the equal-density volume heat source model is well-justified for TIG welding of thin plates, where the arc is relatively stable and the heat input is concentrated. However, several limitations should be noted:

• The model does not account for convective and radiative heat losses from the weld pool surface, which can affect the predicted temperature distribution.
• The thermophysical properties (thermal conductivity, specific heat, density) are assumed to be temperature-dependent, but the accuracy of the property data is critical.
• The model does not simulate the dynamic behavior of the weld pool (fluid flow, surface tension effects), which is important for predicting porosity and solidification defects.

Despite these limitations, the study demonstrates that simplified models can be effective tools for process optimization, particularly in the context of automotive manufacturing where rapid prototyping and process development are essential. The approach is consistent with the PDCA (Plan-Do-Check-Act) cycle: the simulation serves as the "Plan" and "Check" phases, enabling parameter optimization before actual welding trials.