On the assessment of the mechanical properties of additively manufactured lattice structures

Mubasher Ali, Uzair Sajjad*, Imtiyaz Hussain, Naseem Abbas, Hafiz Muhammad Ali, Wei Mon Yan, Chi Chuan Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

38 Scopus citations

Abstract

Lattice structures fabricated by additive manufacturing (AM) technology have many excellent properties, such as lightweight, high strength, energy absorption, and vibration reduction, which have been extensively researched and made a breakthrough. Lattice structures have been commonly used in aviation, bioengineering, robotics, and other industrial fiber because of their outstanding properties. The first part of this article provides a short review on the assessment of mechanical properties of various lattice structures in terms of their classification, applications, materials and fabrication techniques, and complexity of designing, fabrication, and post-processing as well as some of the numerical models to predict the mechanical properties of the lattice structures. The second part of the article proposes a deep learning (DL) model for a highly accurate stress-strain behavior assessment of numerous lattice structures such as namely: the octet, face center-cubic, body-centered cubic, diamond, rhombic, cubic, truncated cube, and truncated cuboctahedron, etc, which were fabricated using many different materials via various approaches and methods. Using the proposed DL model, an accuracy in terms of R2 = 0.999 (correlation coefficient), MSE = 0.0017 (mean squared error), and MAE = 0.0312 (mean absolute error) can be achieved for the prediction of the deemed mechanical property of the lattice structures. The model contains simple, quick and precise predictability that makes it ideal for the use of lattice structures in various practical applications, including heater and heat exchangers, engine hood, biomedical implant, wings, gas turbine, vibration absorber, robotic device, etc.

Original languageEnglish
Pages (from-to)93-116
Number of pages24
JournalEngineering Analysis with Boundary Elements
Volume142
DOIs
StatePublished - Sep 2022

Bibliographical note

Publisher Copyright:
© 2022

Keywords

  • Additive Manufacturing
  • Artificial Intelligence
  • Lattice Structure
  • Machine Learning
  • Mechanical Properties

ASJC Scopus subject areas

  • Analysis
  • General Engineering
  • Computational Mathematics
  • Applied Mathematics

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