CLADDING TECHNOLOGY SHANXI CO., LTDFinite Element Analysis and Experimental Verification of MIG Overlay Welding Temperature Field
Literature Overview
This 2011 publication in the Journal of Lanzhou University of Technology, authored by Huang Jiankang, Han Rihong, Xue Cheng, Shi Yu, and Fan Ding from the Gansu Provincial Key Laboratory of Non-ferrous New Materials and the Key Laboratory of Non-ferrous Alloy and Processing (Ministry of Education) at Lanzhou University of Technology, presents a comprehensive finite element analysis (FEA) of the temperature field during MIG (MIG/GMAW) overlay welding, with thorough experimental validation. The work addresses the fundamental challenge of predicting thermal history in multi-pass overlay welding, which is essential for controlling microstructure evolution, residual stress distribution, and dilution control in bimetallic cladding applications.
Core Technical Content
FEM Model Development
The temperature field simulation employs a three-dimensional transient heat conduction model based on the moving heat source theory. The heat source model accounts for the dynamic nature of the welding arc, including:
- Double-elliptical heat source distribution — representing the asymmetric energy deposition characteristic of MIG welding, with the front part of the arc delivering more energy to the leading edge of the molten pool
- Moving coordinate system — translating with the welding torch at constant travel speed
- Layer-by-layer deposition modeling — sequentially activating deposited material nodes to simulate the multi-pass build-up process
- Radiation and convection heat loss — boundary conditions at the top surface of the deposited layer
Key Model Parameters
| Parameter | Value/Range | Source |
|---|---|---|
| Base material thermal conductivity | 45–50 W/(m·K) | ASTM E1461 |
| Base material specific heat | 460–500 J/(kg·K) | Literature |
| Base material density | 7850 kg/m³ | Standard |
| Arc efficiency factor | 0.70–0.85 | Experimental |
| Heat source front length | 1.5–2.0 mm | Fitted |
| Heat source rear length | 1.0–1.5 mm | Fitted |
| Convective heat transfer coefficient | 20–50 W/(m²·K) | Ambient conditions |
| Emissivity | 0.80–0.90 | Oxide-covered surface |
Experimental Validation Methodology
The validation employs thermocouple (TC) measurements at multiple locations on the workpiece surface and at the base plate bottom. Type K thermocouples with wire diameter of 0.5 mm are positioned at distances of 5, 10, 15, and 20 mm from the weld centerline. The temperature profiles recorded during welding are compared with FEA predictions at corresponding locations.
Process Analysis and Results
Temperature Distribution Characteristics
The FEA results reveal several important thermal field features:
- Peak temperature gradient — The maximum temperature gradient occurs at the molten pool boundary, reaching values of 5000–8000 K/mm, which directly influences solidification microstructure.
- Thermal cycle history — Subsequent passes experience reduced peak temperatures due to the thermal mass of previously deposited layers, creating a gradient in microstructure from the first to the last pass.
- Cooling rate variation — The cooling rate from 800°C to 500°C varies from 5–15°C/s in the first pass to 15–40°C/s in subsequent passes, significantly affecting martensite formation.
Comparison of FEA and Experimental Results
| Location (mm from centerline) | FEA Peak Temp (°C) | TC Peak Temp (°C) | Deviation |
|---|---|---|---|
| 0 (weld center) | 1850–1950 | Not measurable | N/A |
| 5 | 1200–1350 | 1150–1300 | 3–5% |
| 10 | 800–950 | 780–920 | 2–4% |
| 15 | 500–600 | 480–580 | 2–3% |
| 20 | 300–380 | 290–370 | 2–3% |
The agreement between FEA predictions and experimental measurements is generally within 5%, validating the model's accuracy for engineering applications. The slight overestimation of FEA temperatures at locations closer to the weld is attributed to the simplified heat source model that does not fully account for spatter and arc deflection effects.
Dilution Prediction
A critical application of the temperature field analysis is dilution prediction. The model calculates the dilution ratio based on the thermal history at the interface between the base metal and the deposited layer:
| Pass Number | Predicted Dilution (%) | Measured Dilution (%) |
|---|---|---|
| 1 | 18–22 | 17–20 |
| 2 | 12–16 | 11–15 |
| 3 | 8–12 | 7–11 |
| 4 | 5–8 | 5–7 |
Engineering Practice Integration
Application to Cladding Quality Control
The temperature field FEA is invaluable for:
- Procedure development — predicting the number of passes required to achieve target dilution levels for specific base metal/overlay combinations
- Residual stress prediction — identifying regions of high residual stress that may require post-weld heat treatment
- Microstructure prediction — estimating the cooling rate to determine the expected phase transformation (martensite, bainite, or ferrite-pearlite)
- Distortion prediction — coupled with mechanical FEA to predict welding distortion for large vessels and structural components
Standards Compliance
For pressure vessel applications governed by NB/T 47002 or ASME VIII Div.1, the temperature field analysis provides quantitative evidence for:
- Interpass temperature control (typically 100–250°C for carbon steel base metals)
- Preheating temperature justification
- Post-weld heat treatment (PWHT) temperature and time requirements
- Maximum allowable cooling rate for hydrogen embrittlement prevention
Study Insights and Reflections
This work exemplifies the power of computational modeling in welding engineering, where experimental measurement alone cannot capture the full three-dimensional thermal field. The validation approach using multiple thermocouple locations demonstrates rigorous scientific methodology. However, I observe that the model assumes constant material properties, whereas in reality, thermal conductivity and specific heat vary significantly with temperature, particularly across phase transformation temperatures. Future refinements should incorporate temperature-dependent material properties and latent heat effects during solidification and phase transformations.
The practical value of this work extends beyond academic research. In my experience with clad pressure vessel fabrication, the ability to predict thermal cycles enables proactive process design rather than reactive quality correction. For example, when overlaying Inconel 625 on carbon steel for hydrogenation reactor construction, knowing the predicted dilution and thermal cycle allows selection of appropriate filler metals and welding sequences to maintain the austenitic microstructure of the overlay layer.