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Laser Powder Bed Fusion of Ti-6Al-2Sn-4Zr-6Mo Alloy and Properties Prediction Using Deep Learning Approaches.

Hany Hassanin1, Yahya Zweiri2,3, Laurane Finet4

  • 1School of Engineering, Technology and Design, Canterbury Christ Church University, Canterbury CT1 1QU, UK.

Materials (Basel, Switzerland)
|April 30, 2021
PubMed
Summary

This study introduces a deep learning neural network (DLNN) to optimize Laser Powder Bed Fusion for Ti-6Al-2Sn-4Zr-6Mo titanium alloy. The DLNN accurately predicts densification and hardness, enabling efficient material processing for aerospace applications.

Keywords:
additive manufacturingdeep learningporositypowder bed fusion

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Area of Science:

  • Materials Science
  • Additive Manufacturing
  • Machine Learning

Background:

  • Ti-6Al-2Sn-4Zr-6Mo titanium alloy offers high strength, fatigue, and toughness, suitable for aerospace and biomedical uses.
  • Laser Powder Bed Fusion (LPBF) processing of this specific alloy has not been previously studied.
  • Optimizing LPBF parameters is crucial for achieving desired material properties.

Purpose of the Study:

  • To introduce and validate a deep learning neural network (DLNN) for predicting densification and hardness of Ti-6Al-2Sn-4Zr-6Mo during LPBF.
  • To identify optimal LPBF energy density ranges for near-full densification.
  • To explore the effects of process parameters on porosity and hardness.

Main Methods:

  • Development and application of a deep learning neural network (DLNN) model.
  • Laser Powder Bed Fusion (LPBF) experiments on Ti-6Al-2Sn-4Zr-6Mo alloy.
  • Analysis of porosity, hardness, and microstructure.
  • Hot Isostatic Pressing (HIP) for post-processing.

Main Results:

  • Near-full densification achieved with an energy density of 77-113 J/mm³.
  • Hardness increases with laser energy density; porosity is sensitive to island size.
  • Hot Isostatic Pressing (HIP) effectively reduced porosity and enhanced hardness.
  • The DLNN model achieved high accuracy (3% error for porosity, 0.2% for hardness).

Conclusions:

  • Deep learning neural networks are effective for predicting material properties in LPBF, even with limited data.
  • Optimized LPBF parameters and post-processing (HIP) yield high-quality Ti-6Al-2Sn-4Zr-6Mo components.
  • The developed DLNN can generate process maps for efficient alloy fabrication.