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GPU-accelerated meshfree computational framework for modeling the friction surfacing process.

Ahmed Elbossily1, Zina Kallien1,2, Rupesh Chafle2

  • 1Institute for Production Technology and Systems, Leuphana Universität Lüneburg, Universitätsallee 1, 21335 Lüneburg, Germany.

Computational Particle Mechanics
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Summary
This summary is machine-generated.

This study introduces a meshfree Smoothed Particle Hydrodynamics (SPH) framework for friction surfacing (FS) modeling. The GPU-accelerated model accurately predicts thermo-mechanical behavior and deposition mechanisms in aluminum alloys.

Keywords:
Friction surfacingGPU computingMeshless methodsSmoothed particle hydrodynamics

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Area of Science:

  • Computational mechanics
  • Materials science
  • Manufacturing processes

Background:

  • Friction surfacing (FS) is a solid-state additive manufacturing process.
  • Accurate modeling of FS requires robust simulation techniques to capture complex thermo-mechanical phenomena.
  • Existing models may lack the fidelity to precisely predict material deposition and behavior.

Purpose of the Study:

  • To develop and validate a meshfree computational framework for modeling the friction surfacing process.
  • To investigate the thermo-mechanical behavior and deposition mechanisms during FS.
  • To provide a tool for optimizing FS process parameters and understanding material flow.

Main Methods:

  • Utilized the meshfree Smoothed Particle Hydrodynamics (SPH) method for simulation.
  • Implemented GPU computing for enhanced computational efficiency.
  • Integrated optimization techniques (particle switching, sub-domain division) and stability enhancements (artificial viscosity, stress, kernel correction).
  • Developed a novel material separation criterion based on joining temperature and critical shear stress.

Main Results:

  • The SPH model accurately predicted axial force, temperature profiles, and deposit geometries for AA5083 friction surfacing.
  • Simulations revealed key dependencies of process parameters on deposit width and thickness.
  • In-depth insights into material flow, deposition distribution, and rod flash formation were obtained, aligning with experimental data.
  • High plastic strain was observed in the rod flash and deposit, concentrated on the advancing side.

Conclusions:

  • The validated 3D SPH framework is a robust tool for predicting thermo-mechanical behavior in friction surfacing.
  • The model provides valuable insights into deposition mechanisms, aiding in process understanding and optimization.
  • This approach advances the simulation capabilities for solid-state additive manufacturing techniques.