Additive Manufacturing (AM) – or 3D printing – is reshaping how we design and build complex parts. By removing many of the traditional manufacturing constraints, AM puts function first, enabling engineers to create geometries that were once impossible.
 

But here’s the challenge the process itself introduces thermal effects that can cause residual stresses, distortions, or even failures during service. That’s where simulation steps in.

Why simulate Additive Manufacturing?

• Predict and mitigate residual stresses.
• Bridge the gap between designed vs. manufactured parts.
• Evaluate performance under real-world conditions.

Simulation Workflows in Additive Manufacturing Scenario Creation:
1. Thermomechanical Simulation – High-fidelity, physics-based, with heat transfer and stress analysis. Accurate but computationally expensive.
2. Eigenstrain-Based Simulation – Faster, approximate method to capture residual stresses & distortions, calibrated from experiments or detailed simulations.
3. Pattern-Based Thermal-Mechanical – Simplifies laser/toolpath definitions for powder bed processes, balancing accuracy and efficiency.
4. Direct Material Deposition (FDM/LDED) – Simulates nozzle/laser-driven processes where raw material and heat source act simultaneously.

Control at Multiple Scales

• Process-level simulations (fine meshes, small increments) capture detailed physics – but at high computational cost.
• Part-level simulations (coarse meshes, averaged increments) predict distortions/stresses efficiently – ideal for large parts.

With these workflows, engineers can optimize processes, reduce costly trial-and-error, and design for performance rather than manufacturing limitations.

TLDR; Simulation is the missing link that makes Additive Manufacturing reliable at scale.