Three-Dimensional Dynamic Simulation of Temperature Field in Submerged Arc Surfacing on Flat Plates

Literature Overview

This study by Shi Baoshan, He Kuanfang, and He Hezhi, published in Welding Technology (2009, Vol. 38, No. 3, pp. 13–15), presents a finite element analysis (FEA) approach to modeling the temperature field distribution during submerged arc surfacing (SAS) on flat plate substrates. The work addresses a fundamental challenge in welding engineering—the real-time monitoring of temperature distribution during the surfacing process—and proposes a computational model that can predict thermal history at any point in the weldment.

Core Technical Points

Modeling Approach

The authors employ the finite element method (FEM) to establish a three-dimensional dynamic temperature field model for submerged arc surfacing. The model is implemented using ANSYS software with the APDL (ANSYS Parametric Design Language) programming interface, which allows for the automation of the moving heat source analysis. This is a significant technical approach because it enables the simulation of the transient thermal behavior associated with the moving arc without requiring the computationally prohibitive step-by-step manual intervention.

Moving Heat Source Model

The submerged arc welding process involves a continuously moving heat source, which creates a complex three-dimensional temperature field that evolves over time. The model accounts for:

• Gaussian or double-ellipsoidal heat source distribution: The heat input is distributed according to a mathematical function that represents the energy density profile of the submerged arc.
• Convective and radiative heat transfer: The model includes boundary conditions for heat loss to the surrounding environment.
• Phase change effects: The latent heat of fusion is incorporated to accurately capture the solidification behavior of the weld pool and the deposited metal.
• Temperature-dependent material properties: Thermal conductivity, specific heat, and density are functions of temperature, reflecting the complex behavior of steel across the solidification range.

Simulation Results and Validation

The simulation produces temperature field distributions at various time steps during the surfacing process, as well as thermal cycle curves at specific points of interest. The key results include:

1. Temperature field evolution: The three-dimensional temperature contours show the characteristic elongated shape of the weld pool, with the highest temperatures at the arc contact point and progressive cooling in the trailing direction.
2. Thermal cycle curves: Points at different distances from the weld centerline exhibit distinct thermal cycle profiles, with peak temperatures decreasing with distance from the weld.
3. Experimental validation: The simulation results are compared with thermocouple measurements obtained during actual submerged arc surfacing experiments, and good agreement is demonstrated.

Typical Thermal Parameters

Based on the simulation framework, typical thermal parameters for submerged arc surfacing on carbon steel flat plates include:

Engineering Practice Implications

Process Optimization Through Simulation

The primary engineering value of this simulation approach lies in its ability to support process optimization without the need for extensive trial-and-error experimentation. By varying the input parameters in the simulation, engineers can:

1. Predict cooling rates at specific locations, which directly affect the microstructure and hardness of the deposited layer.
2. Identify critical parameters that influence the thermal cycle, such as the interpass temperature and the travel speed.
3. Design multi-pass surfacing sequences that optimize the thermal history for each subsequent pass, ensuring that the final deposit has the desired properties.

Quality Control Applications

The temperature field simulation can be integrated into the quality control process in several ways:

• Predictive assessment: Before production, the simulation can predict whether the thermal cycle will produce acceptable microstructure and properties.
• Process monitoring: Real-time thermocouple readings can be compared with simulation predictions to detect deviations from the expected thermal history.
• Root cause analysis: When defects occur, the simulation can help identify whether the thermal cycle was the contributing factor.

Integration with Microstructure Prediction

The thermal cycle data obtained from the simulation can be used as input for microstructure prediction models. The cooling rate, time above specific temperature thresholds, and peak temperature are the key parameters that determine the phase transformation behavior in the deposited metal. For example:

• Cooling rate above 800°C: Determines the austenite grain size and the tendency for martensite formation in high-carbon deposits.
• Time above 500°C: Affects the precipitation of carbides and intermetallic compounds in alloy deposits.
• Peak temperature: Influences the degree of alloy element dissolution and the homogeneity of the deposit composition.

Key Questions and Reflections

The study demonstrates the feasibility of FEA-based temperature field modeling for submerged arc surfacing, but several questions remain for practical implementation:

1. Computational efficiency: The three-dimensional dynamic simulation requires significant computational resources. For production environments, simplified models or reduced-order models may be necessary to achieve real-time or near-real-time predictions.
2. Model accuracy for multi-pass surfacing: The study focuses on single-pass or simple multi-pass scenarios. For complex multi-layer, multi-pass surfacing operations, the interaction between passes and the cumulative thermal history add significant complexity to the model.
3. Material property data: Accurate simulation requires reliable temperature-dependent material property data, which may not be readily available for all hardfacing alloys. The availability and accuracy of these data sets can be a limiting factor in model development.

Another important consideration is the transition from simulation to practical process control. While the simulation provides valuable insights, the actual welding process is subject to numerous variability factors—fluctuations in arc voltage, variations in flux composition, changes in travel speed, and operator technique—that may not be fully captured in the model. A robust process control strategy should incorporate both simulation-based predictions and real-time monitoring feedback.

Study Insights and Implications

This study represents a significant contribution to the computational modeling of surfacing processes, providing a validated framework that can be adapted for various welding configurations and material systems. The use of APDL programming to automate the moving heat source analysis is particularly noteworthy, as it demonstrates the practical implementation of advanced computational techniques within a commercially available FEA package. For engineers involved in surfacing process development, this work provides a powerful tool for reducing development time and cost while improving the reliability of the final product. The integration of thermal simulation with microstructure prediction and property modeling represents the logical next step in creating a comprehensive computational framework for surfacing process design and optimization.