ANSYS-Based Numerical Simulation of Temperature Field in TIG Arc Additive Manufacturing

Literature Overview

This paper published in Laser & Optoelectronics Progress (2019) by Liu Dongshuai et al. from Jiangnan University presents a finite element analysis of the temperature field during TIG arc additive manufacturing (AM). The authors used ANSYS with Parametric Design Language (APDL) and Jmatpro material database to model the dynamic thermal behavior of single-pass single-layer and multi-layer deposition of ER50-6 carbon steel wire. The work addresses a growing industry need for simulating additive manufacturing processes to optimize build parameters before physical trials.

Methodology and Model Development

The numerical model employs the birth-death element method to simulate the sequential deposition of material layers, which is a standard approach in welding finite element analysis. The heat source is modeled as a Gaussian double-elliptical distribution to capture the asymmetric thermal profile of the TIG arc. Material properties at elevated temperatures were obtained from Jmatpro, ensuring accurate representation of thermal conductivity, specific heat, and density variations across the temperature range encountered during welding.

Key Analytical Results

Substrate Thickness Optimization

The simulation revealed that substrate thickness significantly influences the thermal distribution during deposition. Thinner substrates experience higher peak temperatures and greater thermal gradients, which can lead to excessive distortion and residual stresses. The study identified an optimal substrate thickness that balances thermal management with material utilization. This finding is directly applicable to engineers designing fixtures and backing plates for arc AM builds.

Multi-Layer Temperature Field Behavior

In straight-wall multi-layer deposition, the temperature field exhibits a progressive accumulation effect. As layers are deposited, the thermal history of previously solidified layers changes, influencing microstructure evolution and residual stress development. The cooling rate between layers determines whether the deposited material undergoes complete recrystallization or retains some solidification microstructure, which has direct implications for the final mechanical properties of the build.

Integration with Engineering Practice

The experimental temperature profiles obtained from the simulation provide critical data for post-build thermal treatment planning. Specifically, the authors suggest that the residual heat from deposited layers can be exploited for forging modification of the build part, eliminating or reducing the need for separate heat treatment cycles. This approach reduces production time and cost, which is particularly valuable for large-scale structural components where conventional heat treatment would require extensive furnace capacity.

FMEA Application to Arc AM Process

From a Failure Mode and Effects Analysis (FMEA) perspective, the primary risks in TIG arc AM include thermal distortion exceeding dimensional tolerances, cracking due to high cooling rates in thick sections, and porosity from insufficient arc stability. The numerical simulation serves as a preventive tool, allowing engineers to predict and mitigate these failure modes before physical trials are conducted. The model can be extended to include coupled thermo-mechanical analysis for residual stress prediction, which is essential for ensuring build integrity in critical applications.

Study Insights and Outlook

This work demonstrates the practical value of thermal simulation in arc additive manufacturing process development. The ability to predict optimal substrate thickness and multi-layer thermal behavior without extensive trial-and-error significantly accelerates process qualification. For steel pipe and fitting manufacturing, where complex geometries and thick sections are common, arc AM offers a promising alternative to conventional forming and welding. The simulation framework presented here can be adapted to model pipe fitting fabrication, providing engineers with predictive tools for optimizing deposition strategies and ensuring dimensional accuracy in additively manufactured components.