Machine Learning Customized Novel Material for Energy-Efficient 4D Printing

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Author(s)

  • Chaolin Tan
  • Qian Li
  • Xiling Yao
  • Lequn Chen
  • Jinlong Su
  • Fern Lan Ng
  • Yuchan Liu
  • Youxiang Chew
  • Tarasankar DebRoy

Detail(s)

Original languageEnglish
Article number2206607
Journal / PublicationAdvanced Science
Volume10
Issue number10
Online published5 Feb 2023
Publication statusPublished - 5 Apr 2023

Link(s)

Abstract

Existing commercial powders for laser additive manufacturing (LAM) are designed for traditional manufacturing methods requiring post heat treatments (PHT). LAM's unique cyclic thermal history induces intrinsic heat treatment (IHT) on materials during deposition, which offers an opportunity to develop LAM-customized new materials. This work customized a novel Fe–Ni–Ti–Al maraging steel assisted by machine learning to leverage the IHT effect for in situ forming massive precipitates during LAM without PHT. Fast precipitation kinetics in steel, tailored intermittent deposition strategy, and the IHT effect facilitate the in situ Ni3Ti precipitation in the martensitic matrix via heterogeneous nucleation on high-density dislocations. The as-built steel achieves a tensile strength of 1538 MPa and a uniform elongation of 8.1%, which is superior to a wide range of as-LAM-processed high-strength steel. In the current mainstream ex situ 4D printing, the time-dependent evolutions (i.e., property or functionality changes) of a 3D printed structure occur after part formation. This work highlights in situ 4D printing via the synchronous integration of time-dependent precipitation hardening with 3D geometry shaping, which shows high energy efficiency and sustainability. The findings provide insight into developing LAM-customized materials by understanding and utilizing the IHT-materials interaction. © 2023 The Authors. Advanced Science published by Wiley-VCH GmbH.

Research Area(s)

  • 4D printing, additive manufacturing, green metals, intrinsic heat treatment, machine Learning, new materials, sustainable materials

Citation Format(s)

Machine Learning Customized Novel Material for Energy-Efficient 4D Printing. / Tan, Chaolin; Li, Qian; Yao, Xiling et al.
In: Advanced Science, Vol. 10, No. 10, 2206607, 05.04.2023.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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