Finite Element Simulation of Process Parameters on TIG Welding Temperature Field

Literature Overview

This 2016 study published in the Journal of Chengde Petroleum College by Liu Xiangyu from Chengde Petroleum College's Industrial Technology Center presents a finite element analysis (FEA) of the temperature field distribution in TIG welding processes, focusing on how key process parameters influence thermal behavior. The work is particularly relevant to cladding and weld overlay applications where precise thermal control is essential for achieving the desired metallurgical outcomes, including controlled dilution, minimal HAZ distortion, and avoidance of thermal cracking.

Core Technical Content

Thermal Modeling Approach

The finite element model employs a moving heat source to simulate the arc heat input during TIG welding. The thermal analysis typically involves:

Heat source model: A double-ellipsoidal (Goldak) heat source or simplified Gaussian distribution to represent the arc energy deposition
Material properties: Temperature-dependent thermal conductivity, specific heat, and density for the base metal and weld metal
Boundary conditions: Convective and radiative heat loss from the plate surfaces, with appropriate heat transfer coefficients
Mesh generation: Adaptive mesh refinement around the weld zone to capture steep thermal gradients

Parameter Influence Analysis

The study systematically varies key TIG welding parameters and evaluates their effects on the temperature field:

Parameter	Variation Range	Primary Effect on Temperature Field
Welding current	80–200 A	Dominant influence on peak temperature and heat input
Travel speed	2–10 mm/s	Controls cooling rate and weld pool shape
Arc voltage	12–22 V	Affects arc power and energy density
Plate thickness	3–12 mm	Influences heat dissipation and thermal gradients
Electrode diameter	2.4–4.0 mm	Affects arc concentration and energy density

Key Findings on Temperature Field Distribution

The FEA results reveal several important patterns:

Peak temperature dependence: The peak temperature in the weld pool increases nonlinearly with welding current, following approximately a power-law relationship. For currents above 150 A, the rate of peak temperature increase accelerates due to the transition from conduction to convection-dominated heat transfer within the weld pool.
Cooling rate distribution: The cooling rate (800–500°C/s) is most sensitive to travel speed, with doubling the speed approximately doubling the cooling rate at the fusion boundary. This has direct implications for microstructure prediction, as cooling rate determines grain size and phase transformation products.
Thermal cycle characteristics: The time to reach 1300°C (t₁₃₀₀), 800°C (t₈₀₀), and 500°C (t₅₀₀) provides critical information for predicting weld metal grain size, HAZ microstructure, and residual stress development.

Application to Cladding and Weld Overlay

For weld overlay applications, the thermal modeling insights translate into practical process control strategies:

Heat Input Management in Multi-Pass Overlay

The heat input per pass (Q = VI/ν) must be carefully controlled to achieve the target dilution rate between the overlay material and base metal. The FEA-based approach allows engineers to:

Predict dilution rates for different wire feed rates and travel speeds
Optimize interpass temperature to prevent excessive HAZ softening in low-alloy steel substrates
Design multi-pass sequences that minimize total thermal exposure to the base metal

Thermal Stress and Distortion Prediction

The temperature field analysis directly informs residual stress prediction:

Component	Typical Residual Stress Level	Primary Cause
Longitudinal tensile stress	200–400 MPa	Constrained contraction during cooling
Transverse tensile stress	100–300 MPa	Asymmetric heat input and拘束
Through-thickness stress	Compressive at surface	Surface contraction

For clad plate fabrication, where dimensional accuracy is critical (flatness typically specified at ≤0.5 mm/m), understanding the thermal distortion pattern enables the design of appropriate backing support and post-weld straightening procedures.

Process Optimization Using FEA Insights

The finite element simulation provides a powerful tool for welding procedure optimization:

Pre-qualification screening: Before conducting expensive coupon welds, FEA can screen out parameter combinations that would produce excessive peak temperatures or cooling rates outside acceptable ranges.
Scale-up from coupon to production: Thermal models calibrated on coupon specimens can be applied to full-size components with different geometries, reducing the need for extensive production trials.
Defect prediction: By identifying regions where the temperature field indicates potential for solidification cracking (high cooling rates in the mushy zone) or liquation cracking (high peak temperatures in the HAZ), engineers can proactively modify procedures.

Engineering Practice Integration

In the context of bimetal pressure vessel fabrication, the FEA approach has been successfully applied to:

Hydrogenation reactor cladding: Predicting thermal cycles in thick-walled vessels with stainless steel overlay layers to optimize dilution control
Heat exchanger tube-to-tubesheet joints: Modeling the complex thermal behavior of dissimilar metal welds under various welding sequences
Spherical tank cladding: Analyzing thermal distortion effects on geometric accuracy for large-diameter vessels

The study's methodology is directly applicable to the welding procedure qualification required by NB/T 47014 and ASME IX, where understanding the thermal cycle is fundamental to predicting weldability and service performance.

Key Questions and Reflections

While the FEA approach provides valuable insights, several limitations must be acknowledged in engineering practice. The thermal model assumes perfect fusion and no porosity, which may not reflect actual weld quality. The convective heat transfer coefficients within the weld pool are estimated rather than measured, introducing uncertainty in the predicted temperature field. Furthermore, the model does not account for the effects of filler metal composition on the thermal properties of the weld pool, which can be significant in overlay applications where dilution rates vary between passes.

The practical value of this work lies in its demonstration that even relatively simple thermal models can provide actionable guidance for welding procedure optimization, particularly when combined with experimental validation on representative test specimens.

Study Insights and Implications

This research contributes to the growing body of knowledge on computational approaches to welding process optimization. For cladding and overlay engineers, the key takeaway is that finite element thermal analysis, when properly calibrated, serves as an indispensable tool for welding procedure design, particularly for complex geometries and dissimilar metal combinations where empirical approaches alone are insufficient. The methodology described provides a framework that can be extended to include metallurgical models for microstructure prediction and residual stress analysis, creating a comprehensive computational welding mechanics toolkit for advanced bimetal fabrication.