Abstract
Fused Filament Fabrication (FFF), a process parameters-dependent manufacturing method, currently dominates the additive manufacturing (AM) sector because of its prominent ability to produce parts with intricate profiles, customise products, and minimise waste. Though the effects of FFF process parameters were investigated experimentally, recent research highlighted the importance of developing numerical modelling and computational methods on optimising the FFF printing process and FFF-printed materials. This study aims to investigate the tensile strength (TS) of FFF-printed high-impact polystyrene (HIPS) via devising a systematic testing and analysis framework, which combines experimental testing, representative volume element (RVE)-finite element method (FEM), rule of mixture (ROM), and artificial neural networks (ANN). HIPS samples are fabricated using FFF considering the variations of infill density, layer thickness, nozzle temperature, raster angle, and build orientation, and tested with standard tensile testing. The rule of mixtures (ROM) and its modified version (MROM) are employed to calculate the TS of longitudinally and transversely built samples at various infill densities, respectively, while an ANN model is constructed to investigate the effect of material anisotropy precisely. The optimal ANN architecture is built with five hidden layers with the number of neurons in each layer as 44, 82, 169, 362, and 50. Although both MROM and ANN perform well on the validation set, ANN exhibits superior accuracy with only a maximum error of 0.13% for training set and 11% for validation set. The combination of the RVE-FEM, MROM, and ANN approaches can significantly improve the FFF printing process of polymers for optimisation.
| Original language | English |
|---|---|
| Article number | 107688 |
| Pages (from-to) | 1461-1478 |
| Number of pages | 18 |
| Journal | Progress in Additive Manufacuring |
| Volume | 10 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - Feb 2025 |
Bibliographical note
Publisher Copyright:© The Author(s) 2024.
Open Access - Access Right Statement
This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article�s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article�s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.Keywords
- Artificial neural networks (ANN)
- Finite element method (FEM)
- Fused filament fabrication (FFF)
- High impact polystyrene (HIPS)
- Material anisotropy
- Process parameters
- Representative volume element (RVE)
