Numerical Simulation of Temperature Field in Friction Stir Welding of 6061 Aluminum Alloy and AZ31 Magnesium Alloy Dissimilar Metals

1. Definition and Fundamental Principles

Friction Stir Welding (FSW) is a solid-state joining process in which a non-consumable rotating tool is plunged into the joint line between two workpieces. Frictional heat generated at the tool-workpiece interface raises the material to a superplastic state without melting, and the tool's shoulder and pin mechanically deform and consolidate the softened material into a sound weld. The process is inherently suited to dissimilar metal joining because it avoids the liquid-phase intermetallic compound formation that plagues fusion welding of aluminum-magnesium alloy combinations.

The specific material system under study—6061-T6 aluminum alloy (Al-Mg-Si, approximately 0.6 wt% Mg, 0.45 wt% Si) joined to AZ31 magnesium alloy (Mg-Al-Zn, approximately 3 wt% Al, 1 wt% Zn)—represents one of the most thermally and metallurgically challenging dissimilar combinations in lightweight structural engineering. The two alloys differ significantly in melting point (660°C for 6061 Al vs. 650°C for AZ31 Mg), thermal conductivity (237 W/m·K for 6061 Al vs. 120 W/m·K for AZ31 Mg), specific heat, elastic modulus, and coefficient of thermal expansion. These disparities create asymmetric temperature distributions, differential plastic flow, and complex stress evolution during welding.

Numerical simulation of the temperature field employs Finite Element Method (FEM) or Finite Volume Method (FVM) to solve the transient heat conduction equation with moving heat source and plastic deformation coupling. The governing energy equation is:

ρ·c_p·(∂T/∂t + v_i·∂T/∂x_i) = ∂/∂x_i(k·∂T/∂x_i) + Q_friction + Q_plastic

where ρ is density, c_p is specific heat capacity, T is temperature, v_i is velocity field, k is thermal conductivity, Q_friction is the frictional heat source at the tool-workpiece interface, and Q_plastic is the heat generated by plastic deformation. The simulation captures the transient thermal history, peak temperatures, cooling rates, and thermal gradients that govern the microstructural evolution, residual stress state, and mechanical performance of the final weld.

2. Category and Business Positioning

While Cladding Technology Shanxi Co., Ltd. primarily operates through three established technology routes—TIG/MIG weld overlay, hydraulic explosive bonding, and explosion welding—the numerical simulation capability represented by this entry serves as a critical enabler across all three routes. The company's core business involves dissimilar metal joining and cladding for corrosion resistance, wear resistance, and lightweight structural applications. The ability to model thermal fields provides:

Process development acceleration: Reducing physical trial-and-error cycles for new material combinations
WPS qualification support: Providing analytical evidence for welding procedure specification development and qualification under standards such as ASME Section IX, AWS D1.2, and EN ISO 15614
Customer confidence: Demonstrating technical depth and predictive capability to end users in aerospace, energy, and transportation sectors
Risk mitigation: Identifying potential defects (hot cracking, intermetallic formation, residual stress) before production trials

This entry specifically addresses the aluminum-magnesium dissimilar system, which is directly relevant to the company's lightweight structural cladding and overlay business, particularly for applications where magnesium alloy components must be joined to aluminum alloy substrates.

3. Technical Purpose and Value

The primary technical purpose of simulating the temperature field in FSW of 6061 Al/AZ31 Mg is to understand and control the following phenomena:

3.1 Thermal Asymmetry and Material Flow

Due to the significant difference in thermal conductivity between 6061 Al and AZ31 Mg, the temperature distribution is inherently asymmetric. The aluminum side dissipates heat approximately twice as fast as the magnesium side, resulting in higher temperatures and greater plastic flow on the magnesium side. This asymmetry influences the stir zone geometry, material mixing ratio, and the distribution of intermetallic compounds at the interface.

3.2 Intermetallic Compound Formation Prediction

Al-Mg intermetallic compounds (Mg₁₇Al₁₂, Mg₂Al₃, Mg₅Al₈) are inherently brittle and form readily at the Al/Mg interface when temperatures exceed approximately 200-300°C depending on composition and time. The simulation provides the thermal history (peak temperature, time above critical temperature, cooling rate) necessary to predict intermetallic layer thickness and morphology using kinetics models.

3.3 Residual Stress and Distortion Prediction

The differential thermal contraction between Al and Mg alloys upon cooling generates significant residual stresses. The simulation provides the basis for predicting warpage, distortion, and stress concentrations that could compromise structural integrity or dimensional tolerances.

3.4 Process Parameter Optimization

By systematically varying tool rotation speed, welding speed, plunge depth, shoulder diameter, and tilt angle in the simulation, optimal parameter windows can be identified that minimize peak temperature, reduce intermetallic formation, and achieve adequate bonding—all before committing to physical trials.

4. Key Process and Implementation Points

4.1 Simulation Model Setup

Parameter	6061-T6 Al Alloy	AZ31 Mg Alloy
Density (kg/m³)	2,700	1,810
Thermal Conductivity (W/m·K)	237	120
Specific Heat (J/kg·K)	963	1,023
Melting Point (°C)	660	650
Young's Modulus (GPa)	69	45
Yield Strength (MPa, RT)	276	170
CTE (×10⁻⁶/K)	23.1	26.0

4.2 Heat Source Modeling

The frictional heat source at the tool-workpiece interface is typically modeled using a modified double-ellipsoidal distribution or a simplified cylindrical heat flux distribution:

Q = μ · σ_flow · v_slip · A_contact

where μ is the friction coefficient (typically 0.3–0.5 for Al and Mg alloys in FSW), σ_flow is the flow stress at the elevated temperature, v_slip is the slip velocity between tool and workpiece, and A_contact is the contact area. The friction coefficient varies with temperature, strain rate, and surface condition, requiring iterative coupling with the thermal solution.

4.3 Key Process Parameters for FSW of 6061 Al/AZ31 Mg

Parameter	Typical Range	Optimization Target
Tool Rotation Speed	800–1,500 rpm	Minimize peak temperature while ensuring adequate plasticization
Welding Speed	20–60 mm/min	Balance productivity with thermal input control
Plunge Depth	0.5–1.0 mm (beyond pin length)	Adequate forging pressure without excessive deformation
Shoulder Diameter	14–20 mm	Maximize contact area for heat generation and forging
Tilt Angle	0–3°	Control material flow asymmetry
Pin Length	0.8–1.2 × workpiece thickness	Full penetration with minimal excessive back face protrusion

4.4 Temperature Thresholds and Critical Zones

The simulation identifies several critical temperature thresholds:

Below 150°C: Elastic deformation zone; no significant plastic flow
150–250°C: Mild plastic deformation; limited material mixing; intermetallic nucleation possible at interfaces
250–400°C: Active plastic flow zone; significant material mixing; rapid intermetallic growth in Al-Mg system
Above 400°C: Excessive thermal input; severe intermetallic formation; potential for burn-through or excessive back face protrusion

4.5 Boundary Conditions and Mesh Strategy

A robust simulation requires:

Adaptive mesh refinement in the stir zone and heat-affected zone (element size 0.1–0.5 mm near tool interface)
Convective and radiative heat loss at free surfaces (h = 10–50 W/m²·K for convection; emissivity ε = 0.8–0.95 for oxidation scale)
Tool geometry accurately modeled with pin profile (shouldered, threaded, or flat-bottomed)
Thermo-mechanical coupling for residual stress prediction (sequential or fully coupled approach)
Advection of material properties across the dissimilar interface to capture thermal asymmetry

5. Applicable Standards and Acceptance Criteria

5.1 FSW Process Standards

ISO 22232: Friction stir welding — Welding conditions and procedure qualification
EN ISO 15614-1: Qualification procedures for welding of metallic materials — Friction stir welding
ASME Section IX, QW-302: Qualification of welding procedures (applicable by analogy for solid-state processes)
AWS D10.9: Recommended Practice for Friction Stir Welding
NADCAP AQAP-4111: Friction stir welding process qualification (aerospace)

5.2 Material Standards

ASTM B209: Standard Specification for Aluminum Alloy Extruded Bars, Rods, and Wire (6061)
ASTM B99: Standard Specification for Wrought Magnesium-Aluminum-Zinc Alloys (AZ31)
GB/T 3190: Wrought aluminum and aluminum alloy products
GB/T 8063: Wrought magnesium and magnesium alloy products

5.3 Acceptance Criteria

Criterion	Acceptance Requirement	Verification Method
Joint Strength (Lap Shear)	≥ 60% of base metal AZ31 yield strength	ASTM E8 tensile test
Intermetallic Layer Thickness	≤ 50 μm (continuous), ≤ 20 μm (preferred)	SEM + EDS
Void/Defect Content	No voids > 0.5 mm in stir zone	CT scanning or radiography
Penetration	Full thickness with no back face tunnel	Macrograph examination
Residual Stress	Compressive or near-zero at critical surfaces	X-ray diffraction or neutron diffraction
Corrosion Resistance (Salt Spray)	≥ 500 hours without intergranular corrosion at interface	ASTM B117

6. Common Risks and Controls

6.1 Intermetallic Compound Excessive Growth

Risk: Brittle Al-Mg intermetallic layers (Mg₁₇Al₁₂, Mg₂Al₃) form at the interface when temperatures exceed 200°C for extended durations, creating crack initiation sites that reduce joint strength and fatigue life.

Controls:

Limit peak temperature to below 400°C through simulation-guided parameter selection
Reduce welding speed to increase dwell time only if peak temperature remains controlled
Apply surface coating (e.g., Zn, Al coating on Mg side) to act as diffusion barrier
Use gradient tool materials or consumable insert strategies to control local thermal input

6.2 Thermal Asymmetry and Incomplete Bonding

Risk: The higher thermal conductivity of 6061 Al causes heat to dissipate rapidly on the aluminum side, potentially leaving insufficient plasticization on that side for adequate bonding.

Controls:

Position the aluminum side on the trailing side (retreating side) where material flow is more intense
Apply asymmetric tool geometry (larger shoulder on Al side)
Use pre-heating of the aluminum side to reduce thermal gradient
Employ variable speed FSW with higher rotation speed over the aluminum side

6.3 Residual Stress and Distortion

Risk: Differential thermal expansion and contraction between Al and Mg create residual stresses that may exceed yield strength, leading to cracking during or after welding, or dimensional distortion beyond tolerance.

Controls:

Simulation-based prediction of residual stress distribution to identify critical zones
Post-weld stress relief annealing (for Mg alloy: 150–200°C for 1–2 hours)
Fixture design to constrain distortion during welding
Weld sequence optimization for multi-pass or multi-joint assemblies

6.4 Simulation Fidelity and Validation

Risk: Inaccurate material property models, oversimplified heat source representations, or inadequate mesh resolution can lead to predictions that deviate significantly from experimental reality, resulting in misleading process parameters.

Controls:

Validate simulation against thermocouple measurements, infrared thermography, and metallographic data
Use temperature-dependent material properties (not room-temperature constants)
Perform sensitivity analysis to identify which parameters most influence predictions
Maintain a calibrated database of material properties for each alloy variant and temper

7. Application Scenarios Across the Company's Three Technology Routes

7.1 TIG/MIG Weld Overlay

The temperature field simulation methodology developed for FSW directly transfers to TIG/MIG weld overlay process development. In dissimilar overlay applications—such as depositing corrosion-resistant alloys onto structural substrates—the thermal cycle governs:

Dilution ratio between base metal and filler, which determines overlay composition and properties
Heat-affected zone width and hardness profile
Residual stress magnitude and distribution
Intermetallic compound formation at the overlay-base metal interface

For the specific Al-Mg system, if TIG/MIG overlay is used to create a corrosion-resistant coating on magnesium substrates (using Al-rich filler), the simulation provides critical insight into the thermal cycle that drives intermetallic formation at the interface. The company can leverage this analytical capability to develop WPS documents with quantified thermal parameters, supporting qualification under ASME Section IX or AWS D1.1.

7.2 Hydraulic Explosive Bonding

While hydraulic explosive bonding (HEB) is fundamentally a mechanical process driven by high-strain-rate collision rather than thermal effects, the temperature field simulation contributes in several ways:

Adiabatic shear instability prediction: The high strain rates in HEB generate adiabatic heating that can be modeled to predict the onset of adiabatic shear bands, which are critical for achieving metallurgical bonding at the interface
Post-bond thermal residual stress: The mechanical deformation during HEB generates plastic work that partially converts to heat, and the subsequent cooling creates residual stresses that can be predicted and managed
Material compatibility screening: Thermal property mismatches between bonded materials (as in Al-Mg systems) influence the wave propagation characteristics during the bonding event, and simulation helps predict optimal collision velocities and angles

For lightweight structural cladding of magnesium alloy components with aluminum alloy sheets, the company can use thermal simulation to predict the residual stress state post-HEB and optimize subsequent stress relief treatments.

7.3 Explosion Welding

Explosion welding involves the high-velocity collision of two plates driven by detonated explosives. The temperature field simulation is relevant in the following contexts:

Post-collision thermal analysis: The kinetic energy of the collision is partially converted to heat at the interface, creating a localized temperature spike that influences bonding quality and intermetallic formation. Simulation of this thermal event helps predict intermetallic layer thickness as a function of collision velocity and angle.
Residual stress and distortion prediction: The asymmetric geometry and material properties of Al-Mg explosion-welded cladding create complex residual stress states that can be modeled to predict warpage and dimensional accuracy.
Process parameter optimization: The explosive charge configuration (amount, geometry, detonation sequence) influences the collision velocity and angle, which in turn determine the interfacial temperature and bonding quality. Simulation bridges the gap between explosive design and metallurgical outcome.
Multi-layer cladding design: For multi-layer Al/Mg explosion-welded cladding, the cumulative thermal and mechanical effects of sequential bonding events can be simulated to optimize the bonding sequence and intermediate treatments.

7.4 Cross-Route Integration

The temperature field simulation capability serves as a unifying analytical tool across all three technology routes. For a complex product requiring multiple joining processes—such as an explosion-welded Al/Mg cladding plate with TIG-welded repair or overlay patches, and FSW-sealed edges—the integrated thermal simulation provides:

Sequential thermal cycle analysis accounting for the cumulative thermal history
Prediction of microstructural evolution through multiple thermal events
Optimization of process sequence to minimize total thermal input and residual stress
Comprehensive qualification documentation supporting multi-process WPS

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

8.1 Qualification Building

The numerical simulation capability directly supports the company's qualification program in the following ways:

WPS Development: Simulation provides analytical justification for process parameter selection, reducing the number of physical qualification trials required under ASME Section IX, AWS D1.1, or EN ISO 15614. This accelerates time-to-qualification for new material combinations and reduces qualification costs.
NDE Strategy Optimization: By predicting defect-prone zones (e.g., regions of high residual stress or intermetallic formation), the simulation guides NDE method selection and coverage requirements under ASME Section V or EN ISO 9712.
Customer-Specific Qualification: For aerospace or nuclear customers requiring detailed analytical documentation (e.g., NADCAP, ASME N-stamp), the simulation provides the quantitative evidence needed for regulatory approval.

8.2 Product Delivery

The simulation capability enhances product delivery through:

Reduced scrap rate: By identifying optimal parameter windows before production, the company minimizes trial-and-error waste, particularly important for expensive lightweight alloys like AZ31 Mg and 6061 Al.
Consistent quality: Simulation-derived process windows provide robust operating ranges that maintain quality despite minor variations in material properties or equipment conditions.
Accelerated prototyping: For custom dissimilar metal products, simulation enables rapid virtual prototyping, allowing the company to propose and validate designs before committing to physical fabrication.
Process integration: For complex assemblies requiring multiple joining processes, simulation enables optimal process sequencing and parameter coordination, ensuring consistent quality throughout the manufacturing chain.

8.3 Customer Value

The temperature field simulation capability creates significant customer value:

Technical confidence: Customers in aerospace, energy, and transportation sectors gain confidence in the company's ability to deliver high-integrity dissimilar metal joints, supported by quantitative analytical evidence rather than empirical trial alone.
Design optimization: The company can collaborate with customers during the design phase, using simulation to optimize joint geometry, material selection, and process route for minimum weight, maximum strength, and longest service life.
Cost reduction: By minimizing qualification trials, reducing scrap, and optimizing process parameters, the company delivers competitive pricing without compromising quality.
Regulatory compliance: The analytical documentation generated through simulation supports customer compliance with industry-specific qualification requirements (e.g., FAA, NRC, IAEA), reducing the customer's own regulatory burden.
Innovation leadership: The ability to simulate complex dissimilar metal joining scenarios positions the company as a technical leader capable of addressing novel material combinations and challenging applications that competitors cannot easily replicate.

9. Summary and Forward-Looking Implications

The numerical simulation of temperature fields in FSW of 6061 Al/AZ31 Mg dissimilar metals represents a sophisticated analytical capability that extends well beyond the FSW process itself. It provides the company with a versatile tool for process development, qualification support, quality assurance, and customer engagement across all three technology routes—TIG/MIG weld overlay, hydraulic explosive bonding, and explosion welding.

Looking forward, the integration of temperature field simulation with microstructural evolution modeling (CALPHAD-based phase transformation prediction), residual stress analysis, and fatigue life prediction will create a comprehensive digital twin of the dissimilar metal joining process. This digital twin capability will enable the company to offer customers not just fabricated products, but validated, qualified, and predicted-performance solutions—transforming the company from a manufacturing supplier into a technical partner capable of co-designing lightweight dissimilar metal structures for the next generation of aerospace, energy, and transportation applications.