Artificial Neural Network Algorithms for 3D Printing

• Published in: Materials Vol. 14; no. 1; p. 163
• Main Authors: Mahmood, Muhammad Arif, Visan, Anita Ioana, Ristoscu, Carmen, Mihailescu, Ion N.
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 31.12.2020; MDPI
• Subjects: 3-D printers; Additive manufacturing; Algorithms; Artificial neural networks; Curing; Deformation; Design; Lasers; Learning theory; Machine learning; Mathematical models; Neural networks; Nozzles; Optimization; Process parameters; Rapid prototyping; Residual stress; Review; Simulation; Software; Three dimensional printing; algorithms; artificial neural networks; additive manufacturing; 3D printing
• ISSN: 1996-1944; 1996-1944
• DOI: 10.3390/ma14010163

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.