Dynamic Simulation of Surfacing Thermal Stress Using ANSYS Finite Element Analysis

Literature Overview and Methodology

The paper by Wang Qiang and Li Donglin (2004), published in the Journal of Wuhan University of Technology (Transportation Science and Engineering Edition), presents a finite element analysis (FEA) approach for simulating the transient temperature field, stress field, residual stress, and deformation during plate surfacing welding. The study was conducted at the School of Materials Science and Engineering, Wuhan University of Technology, and the School of Mechanical Engineering, Hubei Institute of Technology. The methodology employs the ANSYS software platform with APDL (ANSYS Programming Language) to implement a moving heat source model that accurately represents the dynamic nature of the welding process.

Finite Element Model Development

The FEA model for surfacing thermal stress simulation requires careful consideration of several modeling aspects:

Geometric model: A plate geometry is established to represent the surfacing substrate. The model dimensions and boundary conditions must be representative of the actual welding conditions while remaining computationally manageable.

Mesh generation: The finite element mesh must be sufficiently refined in the region of the welding heat source to capture the steep temperature gradients, while being coarser in regions far from the weld to reduce computational cost. A common approach is to use a moving mesh or remeshing technique to follow the weld progression.

Material properties: The temperature-dependent material properties are critical for accurate simulation. These include:

Boundary conditions: Convective and radiative heat transfer at the plate surfaces, fixed constraints at appropriate locations to simulate the actual clamping conditions, and the moving heat source representing the welding arc.

Moving Heat Source Implementation

The moving welding heat source is implemented using ANSYS APDL programming language. The heat source model is critical for accurately representing the thermal input during surfacing. Common heat source models include:

1. Gaussian distribution model: Represents the heat flux distribution as a Gaussian function of position, with the peak heat flux at the arc center and exponential decay in all directions. This is the most commonly used model for arc welding processes.
2. Double-ellipsoidal model: Represents the heat source as a double ellipsoid, with different dimensions in front of and behind the arc travel direction. This model better represents the asymmetry of the actual heat input.
3. Convective heat source model: Represents the heat source as a moving point of constant heat input, which is the simplest model but less accurate for detailed analysis.

The APDL program controls the movement of the heat source along the weld path, applying the heat flux at each time step and updating the temperature field accordingly. The time step size must be small enough to capture the transient thermal behavior but large enough to maintain computational efficiency.

Temperature Field and Stress Field Analysis

The simulation produces the following key outputs:

Transient temperature field: The temperature distribution at various time steps during the welding process. The peak temperature at the arc center typically reaches several thousand degrees Celsius, with rapid decay to ambient temperature within a few centimeters of the weld. The temperature field determines the thermal cycle experienced by each material point, which in turn governs the microstructural evolution.

Stress field: The stress distribution resulting from the thermal expansion and contraction during welding. The stress field includes both elastic and plastic components, with plastic deformation occurring in the region where the temperature exceeds the yield temperature. The stress field is highly non-uniform, with compressive stresses near the weld and tensile stresses in the surrounding regions.

Residual stress: The stress distribution remaining after the welding process is complete and the temperature has returned to ambient. Residual stresses are a permanent feature of welded structures and significantly influence the fatigue life, stress corrosion cracking susceptibility, and dimensional stability of the welded component.

Deformation: The permanent deformation of the plate resulting from the welding process. This includes angular distortion, longitudinal shrinkage, and transverse shrinkage, all of which are governed by the asymmetric thermal expansion and the restraint conditions.

Residual Stress Distribution and Engineering Significance

The residual stress distribution in surfacing welds is of critical importance for several engineering reasons:

The FEA simulation provides quantitative predictions of these stress distributions, enabling engineers to evaluate different welding sequences, preheat conditions, and post-weld treatments before actual production welding.

Study Insights and Practical Applications

The FEA approach documented in this paper provides a powerful tool for optimizing surfacing welding processes. By simulating the thermal and stress fields, engineers can:

1. Optimize welding parameters: Determine the optimal combination of heat input, travel speed, and interpass temperature to minimize residual stresses and distortion.
2. Evaluate welding sequences: Compare different welding sequences (such as sequential, symmetric, or skip welding) to identify the sequence that produces the most favorable residual stress distribution.
3. Design preheat and interpass temperature strategies: Quantify the effect of preheat and interpass temperature on the thermal cycle and residual stress distribution.
4. Predict post-weld stress relief effectiveness: Simulate the effect of post-weld heat treatment on residual stress reduction to optimize the PWHT cycle.
5. Validate process changes: Before implementing process changes in production, simulate the expected effects on residual stress and distortion to minimize trial-and-error costs.

The APDL programming approach for implementing the moving heat source is particularly valuable because it allows for flexible and accurate representation of the welding heat input. The ability to customize the heat source model, movement path, and time stepping provides the level of detail necessary for engineering-grade predictions.

This study demonstrates that finite element simulation is an essential tool for modern surfacing welding process development. The predictive capability of FEA reduces the need for extensive trial welding, accelerates process qualification, and provides insights into the thermal and stress behavior that are difficult to obtain through experimental measurement alone. The methodology is directly applicable to surfacing welding on pipes, pressure vessels, and other critical components where residual stress control is essential for ensuring long-term service reliability.

Overall Summary and Cross-Topic Synthesis

The five literature topics examined in this study collectively represent the breadth of technical challenges and innovative solutions in the field of metal overlay welding. From the large-scale electroslag surfacing of hydrogenation reactors with magnetic control devices, to the precision TIG overlay of copper alloy on projectile bodies, to the self-generated carbide particle overlays for enhanced wear resistance, to the innovative open-arc composite powder surfacing process, and to the finite element simulation of thermal stresses, each topic addresses a distinct but interconnected aspect of overlay welding technology.

The common thread running through all five topics is the critical importance of microstructure control in determining overlay performance. Whether the objective is corrosion resistance in hydrogen service, mechanical bonding of dissimilar metals, wear resistance through carbide particle dispersion, or residual stress management through process optimization, the microstructural outcome is the ultimate determinant of functional performance. The electroslag process achieves low dilution to preserve austenitic overlay composition, the TIG process achieves controlled heat input for clean dissimilar metal bonding, the modified electrode flux achieves in-situ carbide formation, the composite powder process achieves controlled microstructural transition through filling rate adjustment, and the FEA simulation predicts the thermal cycle that governs microstructural evolution.

These studies collectively reinforce the principle that overlay welding is not merely a deposition process but a sophisticated metallurgical operation requiring deep understanding of welding metallurgy, materials science, and process engineering. The integration of analytical characterization, process innovation, and computational simulation represents the modern approach to overlay welding development, and the lessons from these five topics are directly applicable to the design and optimization of overlay welding processes for pipes, fittings, and pressure equipment across diverse industrial applications.