ABAQUS-Based Thermal Field Simulation of Dual-Wire Submerged Arc Weld Overlay

1. Definition and Fundamental Principles

ABAQUS-based thermal field simulation of dual-wire submerged arc weld overlay (SAWO) is a computational methodology that employs finite element analysis (FEA) to predict the transient temperature distribution, cooling rates, and thermal cycles experienced in multi-pass overlay welds produced using a dual-wire submerged arc welding process. This capability represents the intersection of metallurgical process engineering and advanced numerical modeling, enabling engineers to virtually characterize the thermal history of clad layers before physical fabrication.

The dual-wire SAW overlay process involves two welding wires feeding simultaneously into a single arc, significantly increasing deposition rates while maintaining arc stability. The thermal simulation models the moving heat source—typically represented by a double Gaussian or double conical heat source distribution—to capture the unique thermal input characteristics of the dual-wire configuration. Key physical phenomena modeled include:

• Heat conduction through the base metal, previous weld passes, and the actively deposited layer, governed by Fourier's law with temperature-dependent thermal conductivity
• Heat convection at the weld pool surface and workpiece boundaries
• Heat radiation from the hot weld pool and subsequent cooling surfaces
• Liquid/solid phase transitions accounting for latent heat of fusion at the solidification front
• Material removal and addition (element birth-and-death technique) to simulate successive weld passes
• Temperature-dependent material properties including density, specific heat, thermal conductivity, and emissivity

The governing energy equation solved in the transient thermal analysis is:

ρcₚ(∂T/∂t) + ρcₚ(v · ∇T) = ∇ · [k(T)∇T] + Q

where ρ is density, cₚ is specific heat capacity, T is temperature, v is the welding speed vector, k(T) is temperature-dependent thermal conductivity, and Q is the volumetric heat source term representing the arc energy input.

2. Category and Business Positioning

Within Cladding Technology Shanxi Co., Ltd.'s technical capability portfolio, this simulation capability occupies a critical position as an enabling technology that supports and de-risks all three primary manufacturing routes:

• TIG/MIG Weld Overlay Route: Provides thermal cycle prediction for multi-pass overlay sequences, enabling optimization of interpass temperature control, weld geometry, and dilution management
• Hydraulic Explosive Bonding Route: Supports thermal post-treatment process design (solution treatment, stress relief) by predicting residual thermal gradients from surface preparation and subsequent weld repair operations
• Explosion Welding Route: Enables modeling of thermal effects from post-bonding machining, welding repairs, and thermal stress relief treatments applied to bonded clad assemblies

This capability positions the company at the forefront of digital twin–enabled manufacturing, where physical welding processes are validated against numerical predictions before production runs, reducing trial-and-error iterations and accelerating WPS (Welding Procedure Specification) qualification.

3. Technical Purpose and Value

3.1 Process Optimization

The primary technical purpose is to determine optimal welding parameters—current, voltage, travel speed, wire feed rate, and interpass temperature—that produce overlay layers meeting specified metallurgical requirements including microstructure, hardness profile, and absence of cracking. By simulating the thermal field, engineers can:

• Predict peak temperatures and cooling rates (t₈₀₀₋₆₀₀, t₅₀₀₋₃₀₀) at critical locations in the overlay
• Identify regions susceptible to hot cracking, cold cracking, or excessive grain growth
• Optimize the number of passes and their sequence to achieve uniform microstructure across the clad layer
• Minimize base metal dilution by controlling thermal input to the interface zone

3.2 Dilution Control

A critical challenge in dual-wire SAW overlay is controlling the dilution ratio—the fraction of base metal melted and incorporated into the overlay layer. The thermal simulation directly correlates welding parameters to dilution by predicting the penetration depth and base metal melting volume. Target dilution ratios for nickel-based overlays (e.g., Stellite, Hastelloy, Inconel) typically range from 15–30%, and the simulation enables precise parameter selection to achieve these targets.

3.3 Residual Stress Prediction

Thermal field results serve as input to subsequent mechanical analyses that predict residual stress distributions in the clad assembly. Understanding residual stress is essential for:

• Assessing distortion and dimensional accuracy of large clad plates and pipes
• Determining the need for stress relief heat treatment and its parameters
• Evaluating risk of hydrogen-induced cracking in high-strength base metals
• Validating design against API 650, ASME Section VIII, and other code requirements for residual stress limits

3.4 Qualification Acceleration

By providing predictive capability, the simulation reduces the number of physical qualification coupons required for WPS qualification under NB/T 47014, ASME Section IX, or AWS D10.9, thereby reducing qualification costs and timelines while maintaining technical rigor.

4. Key Process and Implementation Points

4.1 Thermal Source Modeling

The dual-wire heat source is the most critical modeling element. The recommended approach uses a double Gaussian surface heat source:

Parameter	Description	Typical Range (Dual-Wire SAW)
Heat Input per Wire (Q)	Electrical power delivered by each wire arc	8–20 kW
Wire Diameter (d)	Consumable electrode diameter	1.6–3.2 mm
Travel Speed (v)	Welding speed along the deposition path	0.5–3.0 m/min
Effective Heat Source Radius (a)	Gaussian distribution parameter	3–8 mm
Wire Spacing (s)	Distance between two wire tips	5–15 mm
Efficiency Factor (η)	Fraction of electrical energy transferred to workpiece	0.75–0.85

The volumetric heat source function for each wire i is:

Qᵢ(x,y,z) = (6√3·ηᵢ·Iᵢ·Vᵢ) / (2π√(2π)·aᵢ·bᵢ·cᵢ) · exp(-3[(x-xᵢ)²/aᵢ² + (y-yᵢ)²/bᵢ² + (z-zᵢ)²/cᵢ²])

4.2 Element Birth and Death Technique

Successive weld passes are modeled using ABAQUS's element birth-and-death capability:

1. Initial state: All weld elements are created with reduced stiffness (scale factor = 0.0001) and zero mass
2. Pass deposition: At the start time of each pass, the corresponding element set is "born" (scale factor = 1.0), assigned initial temperature = liquidus temperature, and full material properties are activated
3. Thermal analysis: Heat conduction and convection are solved for the active elements and the surrounding base metal
4. Sequential progression: Each subsequent pass is activated in sequence, with the previously deposited material serving as the substrate for the next pass

4.3 Material Property Data Requirements

Accurate simulation requires temperature-dependent material property data for both the base metal and overlay material:

Property	Temperature Range	Source/Requirement
Thermal Conductivity k(T)	20°C to 1500°C	ASTM E1225, vendor data, or literature
Specific Heat cₚ(T)	20°C to 1500°C	JANAF tables, NIST databases
Density ρ(T)	20°C to 1500°C	Supplier certificates, ISO 12687
Liquidus/Solidus Temperatures	Material-specific	ASTM A396, AWS A5.14, AMS 5528
Emissivity ε	20°C to 1500°C	Empirical (0.6–0.85 for oxidized steel)

4.4 Mesh Configuration and Convergence

The finite element mesh must be configured to capture steep thermal gradients near the weld pool while maintaining computational efficiency:

• Weld zone: 0.5–1.0 mm element size in the direction of travel and transverse direction
• Transition zone: Gradual coarsening to 2–5 mm elements
• Far field: 5–10 mm elements with appropriate boundary conditions
• Element type: 8-node linear brick elements (DC3D8) or 20-node quadratic (DC3D20) for improved accuracy
• Time step: Adaptive time stepping with maximum increment ≤ 0.5 × element size / welding speed

4.5 Boundary Conditions

Location	Condition	Typical Values
Weld pool surface (T > T_liquidus)	Convection + Radiation	h = 5–25 W/m²·K; ε = 0.7–0.85
Cooling surface (T < T_liquidus)	Natural convection + Radiation	h = 5–10 W/m²·K; ε = 0.7
Base/fixed edges	Adiabatic or fixed temperature	T = 20°C or ∂T/∂n = 0
Interface (if layered)	Thermal contact resistance	Rc = 0.001–0.01 m²·K/W

4.6 Validation Against Experimental Data

Simulation credibility is established through validation against experimental thermocouple measurements:

1. Instrument qualification coupons with K-type or N-type thermocouples at multiple depths and distances from the weld centerline
2. Record temperature-time histories at locations corresponding to FE model nodes
3. Compare simulated cooling rates (t₈₀₀₋₆₀₀, t₅₀₀₋₃₀₀) with measured values; acceptable deviation ≤ 20%
4. Validate peak temperature predictions; acceptable deviation ≤ 50°C
5. Document validation results in the WPS technical data package per NB/T 47014 or ASME Section IX

5. Applicable Standards and Acceptance Criteria

5.1 Welding Procedure Standards

• NB/T 47014—2014: Qualification test of welding procedure for pressure vessels and components (thermal cycle data supports procedure qualification)
• ASME Section IX, Part Q: Qualification rules for welding procedures (thermal input calculations per QW-251)
• AWS D10.9M: Specification for welding procedure qualification for overlay welding
• GB/T 9858—2008: Welding procedure qualification rules for steel and nickel alloys
• ISO 15614-1: Qualification testing of welding procedures for metallic materials

5.2 Overlay Welding Standards

• ASTM A396/A396M: Standard specification for alloy steel and nickel alloy castings for pressure-containing parts (overlay requirements)
• ASTM A420: Standard specification for alloy steel and nickel alloy castings for pressure-containing parts
• API 660: Piping components, valves, and fittings with weld overlay
• ISO 14273: Welding—Weld overlaying of metals—Welding consumables
• GB/T 13813: Weld overlay consumables for corrosion and wear resistance

5.3 Simulation Quality Standards

• ASME VDA-1: Verification and validation of computer models for welding (guidance on simulation methodology)
• ISO 23277: Computational fluid dynamics—General guidelines for CFD (applicable principles for thermal simulation)
• AWS D1.1/D1.1M: Structural welding code (thermal distortion acceptance criteria)
• NACE MR0175/ISO 15156: Materials for use in H₂S-containing environments (thermal treatment qualification)

5.4 Acceptance Criteria for Simulation Deliverables

Criterion	Acceptance Threshold	Verification Method
Cooling rate prediction accuracy	≤ 20% deviation from experimental	Thermocouple comparison
Peak temperature prediction	≤ 50°C deviation from experimental	Thermocouple comparison
Weld penetration depth	≤ 15% deviation from radiographic	RT or UT verification
Residual stress (if coupled)	≤ 25% deviation from XRD	X-ray diffraction measurement
Mesh convergence	≤ 5% change with 50% refinement	Mesh sensitivity study
Energy balance	≤ 5% global energy error	ABAQUS output monitoring

6. Common Risks and Controls

6.1 Modeling Risks

Risk	Impact	Control Measure
Inaccurate heat source model	Incorrect thermal profile, misleading predictions	Calibrate against single-pass experimental data; use Rosenthal solution for initial validation
Incomplete material property data	Poor convergence or non-physical results	Use interpolated data from multiple sources; conduct sensitivity analysis on key properties
Inadequate mesh density in weld zone	Smoothing of thermal gradients, inaccurate cooling rates	Perform mesh convergence study; maintain element size ≤ 1 mm in weld pool region
Incorrect element birth timing	Unrealistic thermal history, artificial discontinuities	Validate timing against actual welding sequence; use temperature-dependent activation
Neglecting convection heat loss from weld pool	Overprediction of peak temperatures	Include convective heat transfer coefficient at weld pool surface; validate against thermocouples

6.2 Process Risks Identified Through Simulation

• Excessive cooling rate in thick-section overlays → martensitic transformation in high-carbon base metals → risk of cold cracking; Control: increase preheat, adjust travel speed, optimize interpass temperature
• Overheating at base metal/overlay interface → excessive dilution, loss of corrosion resistance; Control: reduce heat input, increase number of thinner passes
• High residual stress in the clad layer → risk of delamination or hydrogen-assisted cracking; Control: implement stress relief treatment, optimize welding sequence
• Thermal distortion in thin-walled clad pipes → dimensional out-of-tolerance; Control: symmetric welding sequence, backing bars, fixture design

7. Application Across Technology Routes

7.1 TIG/MIG Weld Overlay Applications

The thermal simulation capability directly supports the TIG/MIG weld overlay route in the following ways:

• Multi-pass sequence optimization: For thick overlay layers (5–25 mm), simulate the thermal history of each pass to determine optimal interpass temperature limits (typically 150–300°C for austenitic overlays, ≤ 100°C for martensitic base metals)
• Transition layer design: When overlaying dissimilar metals (e.g., 309L transition layer between carbon steel and 347 stainless), predict dilution and microstructure evolution at the transition layer/base metal interface
• Hot work qualification: For API 660 and NACE MR0175/ISO 15156 compliance, simulate the thermal cycles to ensure post-weld hardness remains within specified limits (typically ≤ 22 HRC for sour service)
• Repair welding prediction: Model the thermal effects of weld repair on previously deposited overlay layers to prevent re-tempering or cracking

7.2 Hydraulic Explosive Bonding Applications

While hydraulic explosive bonding is primarily a cold-forming process, thermal simulation supports adjacent operations:

• Post-bonding weld repair: When localized bonding defects require weld repair, simulate the thermal effects on the bonded interface to prevent debonding or hydrogen embrittlement
• Stress relief thermal treatment: Model the thermal gradients during solution treatment or stress relief of bonded clad plates to ensure uniform microstructure without exceeding bonding interface temperature limits
• Surface preparation welding: For pre-bonding surface preparation using weld removal techniques, predict thermal damage to the base surface
• Edge welding of bonded assemblies: When hydraulic explosive bonded plates require edge welding for fabrication, simulate the thermal effects on the bond quality near the weld zone

7.3 Explosion Welding Applications

For explosion-welded clad products, thermal simulation addresses post-processing operations:

• Post-explosion thermal stress relief: Predict residual stress relaxation during stress relief treatment and identify potential distortion zones
• Weld attachment to clad surfaces: When structural weldments must be attached to explosion-welded clad plates, simulate thermal effects to ensure bond integrity is maintained
• Thermal cycling qualification: For products subjected to thermal cycling in service (e.g., heat exchangers), simulate thermal fatigue effects on the clad layer and bond interface
• Weld overlay on explosion-welded surfaces: When additional overlay is applied on top of explosion-welded cladding, predict the combined thermal history and its effect on bond quality

8. Contribution to Qualification Building, Product Delivery, and Customer Value

8.1 Qualification Building

• WPS Development Support: Thermal simulation provides the engineering justification for parameter selection in Welding Procedure Specifications, reducing the need for extensive coupon testing while maintaining code compliance
• Essential Variable Justification: Simulation results support the technical rationale for proposed ranges of essential variables (heat input, interpass temperature, preheat) in WPS qualification records
• Cross-qualification: Demonstrated simulation capability supports qualification for new material combinations without complete re-qualification of all process variables
• Technical Data Packages: Simulation reports form part of the technical documentation submitted to third-party inspectors and client engineering teams

8.2 Product Delivery Enhancement

• First-time-right production: Predictive capability enables parameter optimization before production, reducing rework and scrap rates
• Large component feasibility: For large clad plates and pipes where full-scale trial fabrication is impractical, simulation provides confidence in process parameters
• Distortion prediction: Pre-production distortion analysis enables fixture design and post-weld straightening planning, ensuring dimensional compliance
• Efficiency optimization: By identifying the minimum number of passes and optimal parameters, simulation reduces production time and consumable costs

8.3 Customer Value

• Technical confidence: Customers receive simulation-backed technical reports demonstrating engineering rigor, supporting their own qualification and regulatory submissions
• Design optimization: Simulation enables collaborative design reviews where thermal performance is evaluated before fabrication commitment
• Performance prediction: Thermal cycle data enables prediction of overlay microstructure and mechanical properties, supporting service life estimation
• Risk mitigation: Early identification of potential thermal issues (cracking, distortion, excessive dilution) prevents costly failures during production or service

9. Implementation Framework

9.1 Workflow

1. Input gathering: Collect base metal and overlay material specifications, geometry data, and target performance requirements
2. Model development: Create FE geometry, assign material properties, configure heat source model, define boundary conditions
3. Mesh generation: Construct converged mesh with appropriate element density in critical regions
4. Analysis execution: Run transient thermal analysis with adaptive time stepping
5. Result extraction: Extract temperature-time histories, cooling rates, peak temperatures, and thermal gradients
6. Validation: Compare with experimental data (thermocouples, dilution measurements, hardness profiles)
7. Iterative optimization: Adjust parameters based on simulation results and re-run until targets are met
8. Report generation: Document model assumptions, results, validation, and recommendations in a formal technical report
9. WPS integration: Incorporate validated parameters into formal Welding Procedure Specification
10. Production release: Transfer optimized parameters to production with appropriate monitoring controls

9.2 Software and Computational Requirements

Component	Specification
Software	ABAQUS Standard (v2022 or later) with thermal module
Preprocessing	ABAQUS CAE or HyperMesh for mesh generation
Post-processing	ABAQUS Visualization module; custom Python scripts for cooling rate extraction
Hardware	Multi-core workstation (≥ 8 cores, ≥ 32 GB RAM) or HPC cluster for large models
Runtime	2–24 hours per complete multi-pass simulation depending on model size

10. Conclusion

ABAQUS-based thermal field simulation of dual-wire submerged arc weld overlay represents a high-value technical capability that bridges computational engineering and manufacturing execution. By providing predictive insight into thermal cycles, cooling rates, dilution behavior, and residual stress development, this capability enables rational process design, accelerates WPS qualification, reduces production risk, and delivers measurable value to customers through first-time-right manufacturing and technically substantiated product performance.

Within the broader context of Cladding Technology Shanxi Co., Ltd.'s multi-route manufacturing strategy, thermal simulation serves as a unifying analytical tool that supports TIG/MIG weld overlay optimization, post-processing thermal treatment design for hydraulic explosive bonded products, and thermal management of explosion-welded clad assemblies. The capability directly contributes to the company's competitive positioning in high-integrity cladding applications across energy, chemical, and oil & gas industries where thermal performance prediction is essential for code compliance and service reliability.