Mishra, A.; Jatti, V.S. Graph Neural Networks (GNN) for Tensile Strength Prediction in Additive Manufacturing. Preprints2023, 2023081007. https://doi.org/10.20944/preprints202308.1007.v1
APA Style
Mishra, A., & Jatti, V.S. (2023). Graph Neural Networks (GNN) for Tensile Strength Prediction in Additive Manufacturing. Preprints. https://doi.org/10.20944/preprints202308.1007.v1
Chicago/Turabian Style
Mishra, A. and Vijaykumar S. Jatti. 2023 "Graph Neural Networks (GNN) for Tensile Strength Prediction in Additive Manufacturing" Preprints. https://doi.org/10.20944/preprints202308.1007.v1
Abstract
This paper presents the use of Graph Neural Networks (GNNs) to predict the tensile strength of Fused Deposition Modeling (FDM) specimens. In the present work, there are four main input parameters i.e. Infill percentage, Layer height, Print speed and Extrusion temperature while the Tensile Strength is an output parameter were considered. This study includes use of central composite design based response surface methodology to finalize the experimental layout. 3D printed specimen were manufactured as per the ASTM E8 standard on FDM printer using Polylactic Acid (PLA) as filament. Micro-tensile test were performed on the printed specimen as per ASTM E8 standard. The GNN algorithm was trained on a dataset of FDM specimens, achieving a mean squared error (MSE) of 2.47, mean absolute error (MAE) of 1.14, and R-squared value of 0.78. An adjacency matrix, which shows the connections between nodes in a graph. The obtained plot for nodes and weights in a GNN provide valuable information about the model and its performance. The results show the potential of using GNNs in predicting the mechanical properties of additively manufactured specimens and provide a promising direction for further research in this field.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
