CLADDING TECHNOLOGY SHANXI CO., LTDFinite Element Solution and Verification of MIG Cladding Temperature Field
Literature Overview
This study presents a comprehensive finite element method (FEM) analysis of the temperature field during MIG (gas metal arc welding) cladding, coupled with experimental verification through thermocouple measurements and infrared thermography. Accurate prediction of the welding temperature field is fundamental to understanding residual stress development, microstructural evolution, distortion, and bond quality in cladding operations. The research bridges the gap between numerical simulation and experimental reality, providing validated models that can be used for process optimization and quality prediction.
Core Technical Content
The study develops a three-dimensional transient thermal model of the MIG cladding process using a moving heat source approach. The model accounts for heat input from the arc, heat dissipation through conduction in both the substrate and deposited material, convective and radiative heat loss from the surface, and the latent heat effects during solidification. The temperature field is solved using the finite element method with adaptive mesh refinement to capture the steep thermal gradients near the weld pool.
Moving Heat Source Model
The study employs a modified double-ellipsoidal heat source model to represent the MIG arc heat distribution:
| Heat Source Parameter | Value / Description | Justification |
|---|---|---|
| Heat source type | Double ellipsoidal (front + back) | Accounts for weld pool geometry asymmetry |
| Front heat distribution factor | 0.6 | 60% of energy deposited in front half |
| Back heat distribution factor | 0.4 | 40% of energy in trailing half |
| Front half-length (a) | 5.0 mm | Matches observed weld pool front width |
| Back half-length (b) | 7.5 mm | Trailing heat zone extends further |
| Half-width (c) | 3.0 mm | Transverse heat distribution |
| Half-depth (d) | 4.0 mm | Penetration depth representation |
| Arc efficiency (η) | 0.80–0.85 | Typical for MIG welding with CO₂ shielding |
| Heat input (Q) | 1.8–3.2 kJ/mm | Depends on current and travel speed |
Material Property Modeling
The thermal model incorporates temperature-dependent material properties for both the substrate and deposited material:
| Property | Substrate (Carbon Steel) | Deposit (Stainless Steel) | Temperature Range |
|---|---|---|---|
| Thermal conductivity (W/m·K) | 50–45 | 25–20 | 20–1200 °C |
| Specific heat (J/kg·K) | 450–800 | 500–750 | 20–1200 °C |
| Density (kg/m³) | 7850 | 7900 | Constant |
| Emissivity | 0.8 | 0.7 | Surface condition dependent |
| Convective heat transfer coefficient (W/m²·K) | 15–25 | 15–25 | Natural + forced convection |
Experimental Verification Methodology
The numerical predictions are validated through multiple experimental techniques:
Thermocouple Placement and Measurements
| Thermocouple Location | Distance from Weld Centerline | Purpose |
|---|---|---|
| TC-1 | 3 mm (transverse) | Peak temperature near weld |
| TC-2 | 6 mm (transverse) | Thermal gradient measurement |
| TC-3 | 10 mm (transverse) | Heat spread zone |
| TC-4 | 15 mm (transverse) | Far-field temperature |
| TC-5 | 20 mm (transverse) | Base temperature reference |
| TC-6 | 3 mm (longitudinal, ahead) | Preheat temperature |
| TC-7 | 3 mm (longitudinal, behind) | Cooling rate measurement |
Comparison of FEM Predictions vs. Experimental Data
| Measurement Point | FEM Peak Temperature (°C) | Experimental Peak Temperature (°C) | Deviation (%) |
|---|---|---|---|
| TC-1 (3 mm transverse) | 845 | 820 | 3.0 |
| TC-2 (6 mm transverse) | 620 | 605 | 2.5 |
| TC-3 (10 mm transverse) | 380 | 370 | 2.7 |
| TC-4 (15 mm transverse) | 210 | 200 | 5.0 |
| TC-6 (3 mm ahead) | 185 | 175 | 5.7 |
| TC-7 (3 mm behind) | 450 | 430 | 4.6 |
The overall deviation between FEM predictions and experimental measurements is within 3–6%, demonstrating good model accuracy for engineering applications.
Cooling Rate Analysis
The cooling rate is a critical parameter governing microstructure formation in cladding welds:
| Distance from Weld Center (mm) | FEM Cooling Rate at 800 °C (°C/s) | Experimental Cooling Rate (°C/s) | Expected Microstructure |
|---|---|---|---|
| 0 (weld center) | 25–35 | 22–30 | Fine martensite / austenite-ferrite |
| 2 | 15–20 | 13–18 | Martensite + retained austenite |
| 5 | 8–12 | 7–10 | Fine grain ferrite + pearlite |
| 10 | 3–5 | 3–5 | Coarse ferrite + pearlite |
| 15 | 1–2 | 1–2 | Coarse ferrite + pearlite |
Process Optimization Using FEM Analysis
The validated FEM model enables systematic optimization of welding parameters for specific cladding requirements:
Parameter Effects on Temperature Distribution
| Parameter Change | Peak Temperature Change | Cooling Rate Change | Heat-Affected Zone Width Change |
|---|---|---|---|
| Current +20% | +80–120 °C | -30% | +25% |
| Travel speed +20% | -60–90 °C | +40% | -15% |
| Wire feed +10% | +40–60 °C | -15% | +10% |
| Preheat +100 °C | +50–70 °C | -25% | +20% |
| Shielding gas change (CO₂→Ar) | -20–40 °C | +10% | -5% |
Engineering Practice Implications
The validated FEM model provides a powerful tool for cladding process development and optimization. Key applications include:
- Residual stress prediction: Temperature field data can be directly used as input for thermo-mechanical coupled analysis to predict residual stress distribution, which is critical for fatigue life assessment.
- Distortion prediction: Thermal gradients drive welding distortion; FEM temperature fields enable prediction of angular and longitudinal distortion in clad plates and pressure vessels.
- Microstructure prediction: Cooling rates from the FEM model can be correlated with expected microstructures using transformation diagrams (TTT/CCT), enabling microstructure-based property prediction.
- Process window determination: Systematic FEM studies can identify parameter combinations that achieve desired thermal conditions for specific cladding applications.
Application to Clad Pressure Vessel Fabrication
For bimetal pressure vessels fabricated with weld overlay cladding, the FEM temperature analysis is particularly important for predicting:
- Dilution at the clad-substrate interface
- Heat-affected zone (HAZ) width and properties
- Residual stress at the metallurgical bond
- Distortion effects on dimensional accuracy
- Number of passes required to achieve specified cladding thickness
Key Questions and Reflections
While the FEM model demonstrates good agreement with experimental data, several limitations remain. The model assumes perfect thermal contact at the clad-substrate interface, but in reality, the interface condition varies with welding parameters and may include partial bonding or gaps. Future work should incorporate interface thermal resistance as a variable to better represent the actual thermal coupling.
Another important consideration is the effect of multi-pass cladding on the thermal history. The temperature field from each subsequent pass superimposes on the residual temperature from the previous pass, creating complex thermal cycling. The study's single-pass model should be extended to multi-pass scenarios for practical multi-layer cladding applications.
The model also does not account for the effect of wire stick-out length on arc characteristics, which can significantly affect heat input distribution. In practice, maintaining consistent stick-out is critical for process stability, and variations in stick-out during production can lead to deviations from the predicted thermal profile.
Study Insights and Implications
This research demonstrates that FEM-based temperature field analysis is a reliable and valuable tool for cladding process development and optimization. The validated model can serve as a foundation for more comprehensive thermo-mechanical simulations that predict residual stress, distortion, and microstructure. For engineers involved in cladding specification and qualification, the ability to predict thermal conditions before actual welding provides significant advantages in reducing trial-and-error costs, optimizing welding sequences, and ensuring consistent quality. The key to successful implementation lies in accurate calibration of material properties and heat source parameters through experimental validation for each specific application.