Numerical Simulation of Temperature Field in TIG Arc Additive Manufacturing Using ANSYS

Literature Overview

This 2019 study published in Laser & Optoelectronics Progress presents a numerical simulation of the temperature field during TIG arc additive manufacturing (AM) using ANSYS with parametric design language (APDL). The research team from Jiangnan University developed a dynamic simulation framework employing the birth-and-death element method to model the sequential deposition of weld beads. The work addresses a growing engineering need: the ability to predict thermal histories in additive manufacturing processes to optimize build parameters and post-build heat treatment strategies.

Simulation Methodology and Model Development

The authors utilized JMatPro software to obtain high-temperature physical property parameters for ER50-6 carbon steel welding wire, which were then incorporated into the ANSYS model. The birth-and-death element method was employed to dynamically activate and deactivate finite elements as material is deposited layer by layer, simulating the sequential build-up of the component.

The model accounts for the heat input from the TIG arc, convective and radiative heat losses from the surface, and the latent heat of fusion during solidification. The dynamic simulation captures the sequential nature of additive manufacturing, where each new layer is deposited onto a previously solidified layer that may still be at elevated temperature.

Key Findings on Temperature Field Distribution

The simulation results reveal several important thermal characteristics of the TIG arc additive manufacturing process:

1. Single-layer single-pass welding: The temperature field exhibits a characteristic asymmetric distribution, with the peak temperature concentrated near the arc contact point and a thermal gradient extending in the direction of welding travel. The cooling rate after arc passage is rapid, leading to steep temperature gradients that can influence microstructure formation.
2. Substrate thickness effects: The study systematically analyzed how different substrate thicknesses affect the temperature field distribution. Thinner substrates exhibit higher peak temperatures and more rapid cooling rates due to reduced thermal mass, while thicker substrates provide better heat dissipation but may require higher energy input to achieve adequate fusion. The optimal substrate thickness was identified as a parameter that balances heat input requirements with thermal distortion control.
3. Multi-layer building: During the construction of straight-walled parts with multiple layers, the temperature field evolves progressively. Each subsequent layer deposits onto a substrate that retains residual heat from previous layers, leading to a cumulative thermal effect that can influence residual stress distribution and distortion patterns.

Engineering Practice Relevance

The most practically valuable finding from this study is the temperature variation pattern obtained from experimental measurements, which provides theoretical guidance for selecting the optimal timing for post-build forging modification. In additive manufacturing of structural components, the residual heat from the welding process can be exploited to perform hot working or forging operations while the material is still at elevated temperature, thereby reducing residual stresses and improving mechanical properties without requiring a separate heat treatment cycle.

For piping applications, this approach could be particularly relevant to the manufacturing of large-diameter pipe fittings or custom spools where traditional forging followed by machining is economically prohibitive. The ability to predict and control the thermal history during AM opens the door to in-situ property optimization.

Study Insights and Reflections

This work represents an important step in the computational modeling of additive manufacturing processes. The use of the birth-and-death element method is a well-established technique in welding simulation that has been successfully adapted to the sequential deposition nature of AM. The validation against experimental data lends credibility to the model predictions.

From my perspective, the key insight is that thermal modeling is not merely an academic exercise but a practical tool for process optimization. The identification of optimal substrate thickness and the prediction of residual heat patterns for forging timing are direct examples of how numerical simulation can guide engineering decisions. However, I would note that the model's accuracy depends critically on the quality of the material property data at high temperatures and the fidelity of the heat source model representing the TIG arc. Future work should incorporate more sophisticated heat source models and account for material property changes due to microstructural evolution during solidification.