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...
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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 |
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