Knowledge Resources How does the 'Remesh surface' process contribute to accuracy? Ensure Reliable Lattice Simulation with Proper Topology
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Tech Team · 3515

Updated 1 week ago

How does the 'Remesh surface' process contribute to accuracy? Ensure Reliable Lattice Simulation with Proper Topology


The 'Remesh surface' process acts as a vital preparation step that bridges the gap between raw geometry and reliable simulation data. It directly contributes to accuracy by cleaning surface defects and integrating small mesh elements to create a uniform topology. This prevents numerical calculation errors and ensures that strain data under load reflects the actual physical behavior of the lattice.

By standardizing the mesh quality and removing geometric anomalies, remeshing eliminates artificial stress concentrations (singularities), ensuring that your simulation results match the true physical stiffness of the part.

Improving Geometric Integrity

Repairing Surface Defects

Raw lattice structures often contain minute geometric imperfections generated during the design or initial meshing phase.

The remesh process actively cleans these defects, creating a watertight and continuous surface. This establishes a valid foundation for any subsequent Finite Element Analysis (FEA).

Achieving Uniform Mesh Quality

Accuracy in simulation relies heavily on the consistency of the mesh elements.

This process integrates small mesh elements across the lattice structure. This integration ensures that the mesh quality is uniform throughout, preventing areas of low resolution that could distort results.

Enhancing Simulation Reliability

Eliminating Numerical Singularities

In simulation, a "singularity" often represents a point where calculations break down due to poor geometry, leading to infinite or unrealistic stress values.

By creating a uniform surface, the remesh process eliminates these potential numerical singularities. This ensures the solver converges on a mathematical solution that represents reality rather than a geometric error.

Verifying Physical Stiffness

The ultimate goal of simulation is to predict how a part will behave in the real world, such as determining the stiffness of footwear soles.

Remeshing ensures that strain data obtained under specific loads—such as a standard 50 N load—accurately reflects the true physical stiffness of the structure. Without this step, the simulation might return artificial stiffness values caused by mesh artifacts rather than the design itself.

The Risks of Bypassing Remeshing

The "Garbage In, Garbage Out" Effect

Skipping the remesh process does not just lower accuracy; it compromises the validity of the entire analysis.

Without a clean, uniform mesh, simulations may produce data that looks correct superficially but is mathematically flawed due to geometric defects.

False Negatives in Performance

In applications like footwear, an un-remeshed model may suggest a lattice is too soft or too stiff due to calculation errors.

This leads to incorrect design decisions, as the strain data does not align with the physics of the actual manufactured part.

Making the Right Choice for Your Goal

To ensure your lattice simulations yield actionable engineering data, consider the following:

  • If your primary focus is Simulation Fidelity: Always apply the remesh surface process to guarantee that strain outputs correlate to true physical stiffness.
  • If your primary focus is Troubleshooting Convergence: Use remeshing to remove geometric singularities that are causing your simulation solver to fail or stall.

Treat surface remeshing not as an optional polish, but as a mandatory prerequisite for valid structural analysis.

Summary Table:

Feature Impact of Remeshing on Simulation Benefit to Result
Geometric Integrity Repairs surface defects and creates watertight models Prevents FEA solver failures
Mesh Uniformity Integrates small elements for consistent topology Eliminates artificial stress concentrations
Numerical Stability Removes geometric singularities Ensures mathematical convergence
Physical Validation Aligns strain data with real-world stiffness Accurate prediction of part performance

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References

  1. Mohammad Javad Hooshmand, Mohammad Abu Hasan Khondoker. Machine Learning Algorithms for Predicting Mechanical Stiffness of Lattice Structure-Based Polymer Foam. DOI: 10.3390/ma16227173

This article is also based on technical information from 3515 Knowledge Base .

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