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Whether solid, liquid, or gas, a substance's state depends on the order and arrangement of its particles (atoms, molecules, or ions). Particles in the solid pack closely together, generally in a pattern. The particles vibrate about their fixed positions but do not move or squeeze past their neighbors. In liquids, although the particles are closely spaced, they are randomly arranged. The position of the particles are not fixed—that is, they are free to move past their neighbors to occupy...
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Smoothing the Rough Edges: Evaluating Automatically Generated Multi-Lattice Transitions.

Martha Baldwin1, Nicholas A Meisel2, Christopher McComb1

  • 1Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.

3D Printing and Additive Manufacturing
|October 3, 2024
PubMed
Summary
This summary is machine-generated.

Creating smooth transitions between different lattice cell types in additive manufacturing is crucial. Variational autoencoders can automate this process by ensuring cell endpoints are close in latent space for better structural integrity.

Keywords:
design for additive manufacturing (DfAM)lattice designmachine learningvariational autoencoders

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Area of Science:

  • Additive Manufacturing
  • Materials Science
  • Computational Design

Background:

  • Additive manufacturing enables lightweight, complex components using unit lattice cells and gradation.
  • Multi-lattice structures with distinct cell types are beneficial for parts with varying loads.
  • Abrupt transitions between unit cell types can lead to stress concentrations and failure.

Purpose of the Study:

  • To demonstrate and assess a method for automating the creation of transitional lattice cells.
  • To identify factors contributing to smooth transitions between different lattice topologies.
  • To address the challenge of achieving smooth transitions in multi-lattice structures, especially between dissimilar cells.

Main Methods:

  • Utilized variational autoencoders (VAEs) to generate transitional lattice cells.
  • Employed computational experimentation to analyze transition smoothness.
  • Examined the relationship between latent space proximity of endpoints and transition smoothness.

Main Results:

  • The proximity of endpoints in the latent space was a strong predictor of transition smoothness.
  • The number of transition intervals was not the sole determining factor for smooth transitions.
  • The developed method automates the creation of transitional lattice cells for multi-lattice structures.

Conclusions:

  • Variational autoencoders offer a viable approach for automating smooth lattice transitions in additive manufacturing.
  • Latent space representation is key to achieving seamless integration between disparate lattice cell types.
  • This method enhances the design of multi-lattice structures, improving overall part functionality and reliability.