Finite Element Simulation of TIG Welding Temperature Field: Effect of Process Parameters

Literature Overview

This paper, published in the Journal of Chengde Petroleum College in 2016, presents a finite element analysis (FEA) approach to understanding the influence of TIG welding process parameters on the temperature field in austenitic stainless steel 0Cr18Ni9 (equivalent to AISI 304). The study establishes both mathematical and physical models and conducts simulations using the ANSYS platform, systematically evaluating the effects of welding current, arc voltage, and travel speed on thermal distribution.

Core Technical Content

Model Development

The finite element model incorporates the following physical phenomena:

• Heat conduction through the workpiece
• Convective and radiative heat loss from the surface
• Moving heat source representing the TIG arc
• Temperature-dependent material properties (thermal conductivity, specific heat, density)

The Gaussian heat source model is typically employed to represent the TIG arc, with the arc power calculated from the product of welding current and arc voltage. The model accounts for the dynamic nature of welding by moving the heat source along the weld path at the specified travel speed.

Key Simulation Results

The simulation confirms that welding current and arc voltage directly determine the total heat input (Q = I × V), thereby exerting the most profound influence on the peak temperature throughout the workpiece. Travel speed, conversely, has a selective effect—significantly influencing peak temperatures in the fusion zone and HAZ while having minimal impact on temperatures at greater distances from the weld line.

Process-Performance Correlation

Heat Input and Microstructural Consequences

The heat input Q (in J/mm) is calculated as Q = (I × V × 60) / v, where I is current (A), V is voltage (V), and v is travel speed (mm/min). For 0Cr18Ni9 stainless steel TIG welding:

• Low heat input (< 15 J/mm): Risk of incomplete fusion, narrow weld bead, potential cracking
• Moderate heat input (15–30 J/mm): Optimal range for microstructure and mechanical properties
• High heat input (> 30 J/mm): Coarse grain growth, reduced strength, increased distortion

Travel Speed Optimization

The finding that travel speed primarily affects the fusion zone and HAZ peak temperatures is practically significant. Increasing travel speed reduces the time available for heat diffusion, resulting in:

• Lower peak temperatures in the immediate weld zone
• Reduced HAZ width
• Steeper temperature gradients
• Potentially higher residual stresses due to rapid cooling

Engineering Application to Pipe Welding

For stainless steel pipe welding in process industries, the FEA approach provides a valuable tool for:

1. Predicting HAZ width before physical welding trials, reducing trial-and-error costs.
2. Optimizing parameter combinations to achieve target weld geometry with minimal distortion.
3. Evaluating the thermal cycle experienced by different weld positions (6 o'clock, 2 o'clock, etc.) in orbital welding applications.
4. Determining appropriate preheat and interpass temperature requirements.

Study Insights and Reflections

This paper demonstrates the power of FEA as a complementary tool to experimental welding studies. While the simulation provides valuable insights into thermal behavior, it must be validated against experimental thermocouple measurements or thermography data. The primary limitation is the assumption of steady-state conditions and the simplification of heat source geometry. For thick-walled pipe welding, three-dimensional transient FEA with appropriate boundary conditions is essential to capture the complex thermal interactions between weld passes and previous heat-affected zones. The findings reinforce the fundamental principle that current and voltage control total energy input, while travel speed governs the spatial distribution of that energy—a distinction that should guide all welding procedure optimization efforts.