Artificial Neural Network Algorithms for 3D Printing

Additive manufacturing with an emphasis on 3D printing has recently become popular due to its exceptional advantages over conventional manufacturing processes. However, 3D printing process parameters are challenging to optimize, as they influence the properties and usage time of printed parts. There...

Full description

Bibliographic Details
Main Authors: Muhammad Arif Mahmood, Anita Ioana Visan, Carmen Ristoscu, Ion N. Mihailescu
Format: Article
Language:English
Published: MDPI AG 2021-12-01
Series:Materials
Subjects:
Online Access:https://www.mdpi.com/1996-1944/14/1/163
id doaj-7f1bdd7201a745ab829100a00b777da0
record_format Article
spelling doaj-7f1bdd7201a745ab829100a00b777da02021-01-01T00:04:42ZengMDPI AGMaterials1996-19442021-12-011416316310.3390/ma14010163Artificial Neural Network Algorithms for 3D PrintingMuhammad Arif Mahmood0Anita Ioana Visan1Carmen Ristoscu2Ion N. Mihailescu3Laser Department, National Institute for Laser, Plasma and Radiation Physics (INFLPR), 077125 Magurele, Ilfov, RomaniaLaser Department, National Institute for Laser, Plasma and Radiation Physics (INFLPR), 077125 Magurele, Ilfov, RomaniaLaser Department, National Institute for Laser, Plasma and Radiation Physics (INFLPR), 077125 Magurele, Ilfov, RomaniaLaser Department, National Institute for Laser, Plasma and Radiation Physics (INFLPR), 077125 Magurele, Ilfov, RomaniaAdditive manufacturing with an emphasis on 3D printing has recently become popular due to its exceptional advantages over conventional manufacturing processes. However, 3D printing process parameters are challenging to optimize, as they influence the properties and usage time of printed parts. Therefore, it is a complex task to develop a correlation between process parameters and printed parts’ properties via traditional optimization methods. A machine-learning technique was recently validated to carry out intricate pattern identification and develop a deterministic relationship, eliminating the need to develop and solve physical models. In machine learning, artificial neural network (ANN) is the most widely utilized model, owing to its capability to solve large datasets and strong computational supremacy. This study compiles the advancement of ANN in several aspects of 3D printing. Challenges while applying ANN in 3D printing and their potential solutions are indicated. Finally, upcoming trends for the application of ANN in 3D printing are projected.https://www.mdpi.com/1996-1944/14/1/163additive manufacturing3D printingartificial neural networksalgorithms
collection DOAJ
language English
format Article
sources DOAJ
author Muhammad Arif Mahmood
Anita Ioana Visan
Carmen Ristoscu
Ion N. Mihailescu
spellingShingle Muhammad Arif Mahmood
Anita Ioana Visan
Carmen Ristoscu
Ion N. Mihailescu
Artificial Neural Network Algorithms for 3D Printing
Materials
additive manufacturing
3D printing
artificial neural networks
algorithms
author_facet Muhammad Arif Mahmood
Anita Ioana Visan
Carmen Ristoscu
Ion N. Mihailescu
author_sort Muhammad Arif Mahmood
title Artificial Neural Network Algorithms for 3D Printing
title_short Artificial Neural Network Algorithms for 3D Printing
title_full Artificial Neural Network Algorithms for 3D Printing
title_fullStr Artificial Neural Network Algorithms for 3D Printing
title_full_unstemmed Artificial Neural Network Algorithms for 3D Printing
title_sort artificial neural network algorithms for 3d printing
publisher MDPI AG
series Materials
issn 1996-1944
publishDate 2021-12-01
description Additive manufacturing with an emphasis on 3D printing has recently become popular due to its exceptional advantages over conventional manufacturing processes. However, 3D printing process parameters are challenging to optimize, as they influence the properties and usage time of printed parts. Therefore, it is a complex task to develop a correlation between process parameters and printed parts’ properties via traditional optimization methods. A machine-learning technique was recently validated to carry out intricate pattern identification and develop a deterministic relationship, eliminating the need to develop and solve physical models. In machine learning, artificial neural network (ANN) is the most widely utilized model, owing to its capability to solve large datasets and strong computational supremacy. This study compiles the advancement of ANN in several aspects of 3D printing. Challenges while applying ANN in 3D printing and their potential solutions are indicated. Finally, upcoming trends for the application of ANN in 3D printing are projected.
topic additive manufacturing
3D printing
artificial neural networks
algorithms
url https://www.mdpi.com/1996-1944/14/1/163
work_keys_str_mv AT muhammadarifmahmood artificialneuralnetworkalgorithmsfor3dprinting
AT anitaioanavisan artificialneuralnetworkalgorithmsfor3dprinting
AT carmenristoscu artificialneuralnetworkalgorithmsfor3dprinting
AT ionnmihailescu artificialneuralnetworkalgorithmsfor3dprinting
_version_ 1724364556796428288