ANSYS-Based Numerical Simulation and Verification of Bimetallic Composite Casting Processes

1. Definition and Fundamental Principles

1.1 Scope of the Technology

ANSYS-based numerical simulation and verification of bimetallic composite casting processes represents a computational engineering methodology that applies finite element analysis (FEA) to predict, optimize, and validate the metallurgical and mechanical behavior of bimetallic composite castings. This technology leverages the multi-physics capabilities of the ANSYS software suite—encompassing thermal analysis, fluid dynamics (melt flow), structural mechanics, and phase transformation modeling—to simulate the entire casting sequence from mold filling through solidification, cooling, and residual stress development in bimetallic composite structures.

The core principle involves translating the physical casting process into a mathematical model governed by coupled heat transfer, fluid flow, and stress-strain equations. The simulation captures the interaction between two or more dissimilar metallic alloys during the composite casting operation, predicting critical phenomena including intermetallic compound formation, thermal mismatch stresses, segregation patterns, and potential defect initiation sites at the interface between the base metal and the overlay/functional layer.

1.2 Governing Physics in Bimetallic Composite Casting Simulation

• Thermal Analysis: Solves the transient heat conduction equation with temperature-dependent material properties (thermal conductivity, specific heat, density) for each alloy system. Accounts for latent heat release during solidification of both the substrate and the cladding alloy.
• Melt Flow Dynamics: Models the fluid mechanics of molten metal filling the mold cavity using Navier-Stokes equations with appropriate boundary conditions for free surfaces, accounting for the density differences between the two alloy systems that drive stratification or mixing.
• Microstructural Evolution: Predicts solidification morphology, grain orientation, and interfacial reaction layer formation using thermodynamic databases (CALPHAD-based) coupled with kinetic models for diffusion-controlled intermetallic growth.
• Thermo-Mechanical Stress Analysis: Computes residual stresses arising from differential thermal contraction between the two dissimilar materials, constrained shrinkage, and phase transformation strains during cooling from the as-cast condition to room temperature.

2. Category and Business Positioning

2.1 Positioning Within the Technology Portfolio

This capability is classified as a supporting engineering and qualification technology that underpins all three primary manufacturing routes of the company: TIG/MIG weld overlay, hydraulic explosive bonding, and explosion welding. While the direct application addresses bimetallic composite casting (a related but distinct process route involving the simultaneous or sequential solidification of two alloys), the simulation methodology, analytical frameworks, and validation protocols developed through this work transfer directly to the qualification and optimization of weld overlay and explosive bonding processes.

2.2 Strategic Value in the Business Chain

• Pre-qualification Engineering: Enables virtual qualification of new alloy combinations and process parameters before committing to physical trial production, reducing qualification cycle time by 30–50%.
• Design-for-Manufacture: Provides engineering justification for product geometry, section thickness ratios, and cooling rates that ensure metallurgical soundness of the bimetallic interface.
• Customer Technical Support: Generates validated simulation reports that serve as engineering documentation for customer approvals, particularly in nuclear, energy, and critical infrastructure applications requiring rigorous design qualification.
• Process Optimization: Identifies optimal pouring sequences, cooling rates, and thermal management strategies that minimize interfacial defects and residual stresses.

3. Technical Purpose and Value

3.1 Primary Technical Objectives

The primary objective of ANSYS-based numerical simulation for bimetallic composite casting is to establish a validated predictive model that accurately represents the physical process, enabling engineering decisions with quantified confidence. Specific objectives include:

1. Interface Quality Prediction: Determine the width, composition, and morphology of the interfacial reaction zone (diffusion zone) between dissimilar alloys as a function of holding temperature, holding time, and cooling rate.
2. Defect Prevention: Identify conditions that promote hot tearing, porosity, segregation, and cracking at the bimetallic interface during solidification and cooling.
3. Residual Stress Mapping: Quantify the magnitude and distribution of residual stresses in the as-cast condition and after subsequent thermal treatments.
4. Process Window Definition: Establish the allowable ranges of process parameters (pouring temperature, mold preheat, cooling rate, holding time) that produce acceptable metallurgical quality.

3.2 Quantifiable Engineering Value

Value Metric	Without Simulation	With Validated Simulation	Improvement
Qualification trial cycles	5–8 physical trials	2–3 physical trials	60–70% reduction
Time to first qualified product	4–6 months	2–3 months	50% cycle reduction
Scrap rate during qualification	25–40%	5–10%	75% reduction
Engineering confidence in design	Empirical/iterative	Quantified/predictive	Qualitative improvement
Customer approval documentation	Test-only	Simulation + test validated	Complete engineering package

4. Key Process and Implementation Points

4.1 Simulation Workflow

A rigorous ANSYS-based simulation of bimetallic composite casting follows a structured workflow consisting of the following sequential stages:

1. Problem Definition: Identify the specific bimetallic system (e.g., carbon steel substrate with stainless steel overlay, copper base with nickel-aluminum bronze cladding), the casting configuration (sand casting, investment casting, centrifugal casting), and the performance requirements.
2. Geometry Modeling: Create the 3D CAD model of the mold cavity, risers, gates, and the bimetallic arrangement. Apply appropriate mesh refinement at the interface region (element size typically 0.5–2 mm at the interface vs. 5–10 mm in bulk regions).
3. Material Property Assignment: Define temperature-dependent properties for each alloy including density, thermal conductivity, specific heat, liquidus/solidus temperatures, solidification range, elastic modulus, thermal expansion coefficient, and creep behavior.
4. Boundary and Initial Conditions: Specify mold preheat temperature, pouring temperature, ambient conditions, and heat transfer coefficients for mold-metal and mold-environment interfaces.
5. Multi-Physics Coupling: Execute sequential or fully coupled analysis: thermal analysis first to obtain temperature histories, followed by structural analysis using the temperature field as thermal load, with appropriate constraint conditions representing mold restraint and phase transformation.
6. Post-Processing and Interpretation: Extract temperature-time curves at critical locations, solidification time maps, residual stress distributions, and interface reaction zone predictions.
7. Experimental Validation: Compare simulation predictions with physical test results (thermocouple data, metallographic examination, XRD analysis, residual stress measurement) and calibrate the model.

4.2 Critical Simulation Parameters and Their Influence

Parameter	Typical Range	Influence on Interface Quality	Sensitivity
Pouring temperature	1450–1650°C	Higher T increases diffusion zone width and intermetallic thickness	High
Mold preheat temperature	200–600°C	Controls initial cooling rate and thermal gradient at interface	Medium-High
Interface holding time	0–120 min	Directly controls diffusion zone width (proportional to √t)	High
Cooling rate	1–50°C/min	Affects solidification microstructure and residual stress magnitude	Medium
Section thickness ratio	1:1 to 5:1	Determines thermal asymmetry and stress concentration at interface	Medium
Heat transfer coefficient (mold-metal)	100–800 W/m²·K	Controls solidification front progression and feeding behavior	High

4.3 Model Validation Protocol

Model validation is essential to establish predictive credibility. The validation protocol includes:

• Thermal validation: Instrument trial castings with thermocouples at multiple locations (substrate center, interface, overlay surface, mold wall). Compare measured temperature-time curves with simulated predictions. Acceptable accuracy: within ±30°C for peak temperatures and ±2 minutes for time-to-solidus.
• Metallographic validation: Measure the actual diffusion zone width and intermetallic layer thickness via optical microscopy and SEM-EDS line scans. Compare with simulated diffusion profiles. Acceptable accuracy: within ±20% of predicted width.
• Residual stress validation: Measure as-cast residual stresses using X-ray diffraction (XRD) or hole-drilling methods at defined locations. Compare magnitude and gradient with FEA predictions. Acceptable accuracy: within ±25% for stress magnitude.
• Defect validation: Document actual defect occurrence (porosity, cracking, segregation) via NDT and destructive testing. Verify that simulation correctly predicted defect-free zones and identified risk locations.

5. Applicable Standards and Acceptance Criteria

5.1 Governing Standards

Standard	Relevance to Simulation and Verification
GB/T 18449-2016	Composite steel plates — General technical conditions; defines acceptance criteria for interface quality that simulation must predict
GB/T 25061-2010	Composite steel pipes — Technical conditions; specifies bonding strength requirements validated through simulation-predicted interface properties
ASTM A393/A393M	Standard Specification for Composite Steel Plates, Sheet, Strip, and Clad Plate; acceptance criteria for composite bonding
ASTM A828/A828M	Standard Specification for Composite Steel Plates, Sheet, Strip, and Clad Plate; includes testing requirements
ASME BPV Section II, Part D	Qualification requirements for materials and processes in pressure vessels; simulation data may support material qualification
NB/T 20305-2007	Design specification for nuclear power plant pressure boundaries; requires rigorous metallurgical qualification supporting nuclear-grade composite materials
ISO 14555-1:2009	Welding — Weld overlay — Part 1: General recommendations; provides framework for overlay process qualification that simulation methodology supports
API 5L	Specification for line pipe; relevant for composite pipe applications where simulation validates corrosion-resistant overlay performance
NACE SP0169	Impressed current cathodic protection design criteria; simulation data supports understanding of galvanic effects in bimetallic systems
ASTM E831	Standard Practice for Estimating Residual Stress by the Hole-Drilling Strain-Gauge Method; validation method for simulation predictions
ISO 17640	Welding — General guidelines for the qualification of welding procedures for metallic materials; framework for process qualification supported by simulation

5.2 Simulation-Specific Acceptance Criteria

1. Thermal prediction accuracy: Maximum deviation between simulated and measured temperature at any instrumented point shall not exceed ±30°C during solidification range and ±50°C outside solidification range.
2. Solidification time prediction: Time from liquidus to room temperature at critical locations shall be predicted within ±15% of measured values.
3. Residual stress prediction: Peak residual stress magnitude at the interface shall be predicted within ±30% of measured values, with correct stress state (tensile vs. compressive).
4. Diffusion zone prediction: Interfacial reaction zone width shall be predicted within ±25% of measured values for the specific thermal history.
5. Defect prediction: Zero false negatives — all actual defects observed in trial castings must be predicted as risk locations in the simulation.

6. Common Risks and Controls

6.1 Simulation-Specific Risks

Risk	Description	Mitigation Control
Material property uncertainty	Temperature-dependent properties may be estimated or interpolated from similar alloys	Use experimentally measured properties where possible; perform sensitivity analysis quantifying property uncertainty impact on results
Boundary condition simplification	Radiation, convection, and mold contact heat transfer are simplified	Calibrate heat transfer coefficients against thermocouple data from trial runs; use inverse analysis for HTC determination
Mesh dependency	Results may be sensitive to mesh density, particularly at the interface	Perform mesh convergence study; refine to 0.5 mm at interface; document mesh independence verification
Phase transformation modeling	Incomplete or inaccurate representation of solidification and solid-state transformations	Use validated solidification models (e.g., Scheil-Gulliver, lever rule); incorporate transformation kinetics from DSC data
Over-reliance on simulation	Engineering decisions made solely on simulation without physical verification	Establish mandatory physical validation for any new alloy system or process configuration; simulation supports but never replaces physical testing
Software version changes	Different ANSYS versions may produce different results for the same input	Document software version and solver settings; maintain model reproducibility; periodic re-validation with updated versions

6.2 Metallurgical Risks Addressed by Simulation

• Interfacial cracking: Simulation identifies locations and thermal histories that produce excessive thermal mismatch stresses exceeding the interface fracture toughness. Controls include optimized cooling rates, thermal barrier design, and post-cast stress relief parameters.
• Excessive intermetallic formation: Simulation predicts diffusion zone width as a function of time-temperature exposure. Controls include limiting holding times, optimizing interface temperature, and selecting alloy systems with lower mutual diffusivity.
• Hot tearing: Simulation identifies regions of high thermal gradient combined with constrained shrinkage during the solidification range. Controls include riser design optimization, pouring sequence modification, and mold material selection.
• Segregation at interface: Simulation with coupled fluid-thermal models identifies conditions promoting macrosegregation at the bimetallic boundary. Controls include controlled pouring velocities, appropriate mold geometry, and directional solidification strategies.

7. Application Across the Three Technology Routes

7.1 TIG/MIG Weld Overlay Applications

While the simulation technology is developed for composite casting, the analytical methodology transfers directly to weld overlay process optimization:

• Thermal cycle prediction: ANSYS transient thermal analysis models the heat input, temperature distribution, and cooling rates for multi-pass weld overlay sequences. This enables prediction of HAZ microstructure evolution, dilution rates, and interpass temperature requirements for weld overlay qualification under ISO 14555 and ISO 15614.
• Residual stress analysis: Sequential thermo-mechanical simulation of multi-layer, multi-pass weld overlay predicts residual stress buildup and identifies optimal pass sequencing, interpass temperature control, and post-weld stress relief parameters. This directly supports qualification testing under ASME BPV Section IX and GB/T 19418.
• Interface diffusion prediction: For thick overlay layers or high-temperature service applications, simulation predicts the evolution of diffusion zones at the base metal/weld interface during long-term service exposure, supporting lifetime assessment and maintenance planning.
• WPS optimization: Simulation identifies the process window (heat input, travel speed, wire feed rate, preheat temperature) that produces acceptable metallurgical quality while minimizing distortion and residual stress, reducing the number of physical WPS qualification trials required.

7.2 Hydraulic Explosive Bonding Applications

For hydraulic explosive bonding (water jet explosive welding), simulation technology supports the following engineering activities:

• Post-bonding stress analysis: After the bonding event, the bimetallic laminate undergoes rapid cooling from the high temperatures generated at the interface. Simulation predicts the residual stress state in the bonded laminate, identifying potential delamination risks and informing post-bonding heat treatment requirements.
• Thermal treatment optimization: For bonded laminates requiring solution treatment or stress relief, simulation predicts the stress evolution during thermal cycles and identifies optimal treatment parameters to achieve target residual stress levels while maintaining interface integrity.
• Component design validation: For bonded components that will be machined, formed, or subjected to service loads, simulation provides the residual stress baseline for subsequent mechanical analysis, ensuring that forming limits and service life are not compromised by bonding-induced stresses.
• Multi-material system qualification: When bonding unusual material combinations (e.g., dissimilar copper alloys, aluminum-to-steel), simulation provides preliminary assessment of interface metallurgy and stress state before committing to physical bonding trials, supporting qualification under GB/T 18449 and ASTM A393.

7.3 Explosion Welding Applications

For explosion welding, the simulation methodology contributes to the following qualification and optimization activities:

• Post-explosion thermal analysis: The explosive welding event generates significant heat at the bonding interface. Simulation models the post-event cooling history to predict the solidification microstructure of the bonding zone, the width of the diffusion layer, and the residual stress distribution in the final bonded plate.
• Service condition assessment: For explosion-welded components intended for elevated-temperature service (e.g., nuclear pressure boundaries under NB/T 20305, power generation components under ASME BPV), simulation predicts the evolution of interfacial microstructure and stress state during long-term thermal exposure, supporting lifetime qualification.
• Post-welding process simulation: After explosion welding, components often undergo rolling, annealing, or stress relief. Simulation predicts the interaction between welding-induced residual stresses and subsequent deformation/thermal cycles, ensuring that final product meets flatness, dimensional, and metallurgical requirements.
• Failure mode analysis: Simulation supports post-incident analysis of bonded component failures by reconstructing the stress state and identifying the root cause mechanism (delamination, interface cracking, intermetallic embrittlement) through reverse engineering of observed failure patterns.

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

8.1 Qualification Building

The ANSYS-based simulation capability directly accelerates and strengthens the company's qualification portfolio:

• Reduced qualification timelines: By pre-identifying viable process windows through simulation, the number of physical qualification trials is reduced from 5–8 to 2–3, cutting qualification project duration by 40–60%. This is particularly valuable for nuclear and energy sector qualifications where project schedules are tightly constrained.
• Comprehensive engineering documentation: Validated simulation reports provide the analytical basis that complements physical test data in qualification packages. Regulatory bodies and customer engineering teams increasingly expect computational evidence alongside physical testing, particularly for novel material combinations or unconventional geometries.
• Scalability to new applications: Once a simulation model is validated for a specific material system and process, it can be rapidly adapted to different geometries, thicknesses, and configurations, enabling rapid qualification expansion without proportional increases in trial production costs.

8.2 Product Delivery Enhancement

• First-time-right manufacturing: Simulation-optimized process parameters result in higher first-pass yield rates during production, reducing rework and scrap. For high-value composite components (nuclear-grade clad plates, large-diameter composite pipes), this translates to significant cost savings and schedule assurance.
• Consistent quality assurance: Simulation establishes the theoretical process envelope within which acceptable quality is guaranteed. Production parameters are maintained within this validated envelope, providing a scientific basis for quality control beyond empirical inspection.
• Non-destructive evaluation support: Simulation-predicted defect risk locations guide NDT planning, enabling targeted inspection rather than blanket examination. This improves inspection efficiency while maintaining or exceeding coverage requirements under GB/T 24776 and ASME V.

8.3 Customer Value Creation

• Engineering confidence: Customers in critical infrastructure sectors (nuclear, oil & gas, power generation) require rigorous engineering justification for material and process selections. Validated simulation reports provide this justification, accelerating customer approval and reducing contractual risk.
• Lifetime performance prediction: Simulation extends beyond as-manufactured condition to predict long-term performance evolution (stress relaxation, interfacial diffusion, creep interaction). This supports customer asset management planning and condition monitoring strategy development.
• Design optimization partnership: The simulation capability enables collaborative design optimization with customers, offering engineering services that differentiate the company from pure manufacturing competitors. Customers gain access to computational engineering expertise for their specific applications.
• Regulatory compliance support: For nuclear applications governed by NB/T 20305 and international equivalents (ASME BPV III), simulation documentation supports regulatory submissions and provides traceability from design basis to manufacturing parameters.

9. Implementation Recommendations

9.1 Model Development Priority Matrix

Priority Level	Model Type	Target Application	Timeline
High (Immediate)	Thermal analysis of weld overlay sequences	TIG/MIG overlay qualification acceleration	0–3 months
High (Immediate)	Post-bonding residual stress analysis	Explosive bonding/hydraulic bonding qualification	0–3 months
Medium (Near-term)	Coupled thermal-mechanical stress analysis	Multi-pass overlay optimization, post-bonding forming	3–6 months
Medium (Near-term)	Diffusion zone prediction model	Long-term interface stability assessment	3–6 months
Long-term	Full multi-physics coupled simulation	End-to-end process optimization, lifetime prediction	6–12 months

9.2 Competency Development

1. Software proficiency: Train dedicated engineers in ANSYS Mechanical (for structural/thermal), ANSYS Fluent (for melt flow), and ANSYS Granta (for materials data management). Minimum competency: independent model development and validation for standard configurations.
2. Materials characterization: Establish capability to measure temperature-dependent properties (thermal conductivity, specific heat, elastic modulus) for each alloy system used in production, ensuring simulation input data is specific and accurate.
3. Validation infrastructure: Equip laboratory with thermocouple instrumentation capability, XRD residual stress measurement, SEM-EDS for interface characterization, and digital image correlation for deformation measurement.
4. Knowledge management: Maintain a validated model library organized by material system, process type, and geometry configuration. Document all validation data, assumptions, and limitations for traceability and reuse.

10. Conclusion

ANSYS-based numerical simulation and verification of bimetallic composite processes represents a critical enabling technology that amplifies the company's manufacturing capabilities across all three technology routes. By providing predictive engineering analysis that complements physical trial production, this technology reduces qualification timelines, improves manufacturing yield, generates comprehensive engineering documentation for customer and regulatory approval, and creates differentiated value through computational engineering expertise. The systematic implementation of this capability—prioritized by immediate application to weld overlay thermal analysis and explosive bonding post-process stress analysis—will yield measurable improvements in qualification efficiency, product quality consistency, and customer engineering confidence within 6–12 months of focused development.