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Artificial Neural Network Algorithms for 3D Printing.

Muhammad Arif Mahmood1,2, Anita Ioana Visan1, Carmen Ristoscu1

  • 1Laser Department, National Institute for Laser, Plasma and Radiation Physics (INFLPR), 077125 Magurele, Ilfov, Romania.

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|January 5, 2021
PubMed
Summary
This summary is machine-generated.

Artificial neural networks (ANNs) optimize 3D printing by identifying complex patterns, improving part properties and lifespan. This machine learning approach overcomes challenges in traditional 3D printing parameter optimization.

Keywords:
3D printingadditive manufacturingalgorithmsartificial neural networks

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Area of Science:

  • Materials Science
  • Manufacturing Engineering
  • Computer Science

Background:

  • Additive manufacturing, particularly 3D printing, offers significant advantages over conventional methods.
  • Optimizing 3D printing process parameters is crucial for part properties and longevity but is complex.
  • Traditional optimization methods struggle to establish correlations between parameters and part performance.

Purpose of the Study:

  • To review the advancements of artificial neural networks (ANNs) in 3D printing applications.
  • To identify challenges and propose solutions for implementing ANNs in 3D printing.
  • To project future trends for ANNs in additive manufacturing.

Main Methods:

  • Literature review compiling studies on ANN applications in 3D printing.
  • Analysis of ANN's capability in pattern identification and deterministic relationship development.
  • Discussion of challenges and potential solutions for ANN integration.

Main Results:

  • ANNs offer a powerful machine learning technique for intricate pattern identification in 3D printing.
  • ANNs can establish deterministic relationships, bypassing the need for complex physical models.
  • The study highlights the growing role of ANNs in optimizing 3D printing processes.

Conclusions:

  • ANNs are a valuable tool for overcoming the complexities of 3D printing parameter optimization.
  • Addressing current challenges will further unlock the potential of ANNs in additive manufacturing.
  • Future research should focus on expanding ANN applications and exploring emerging trends in 3D printing.