Modeling and analysis of flexural strength with fuzzy logic technique for a fused deposition modeling ABS components

• Published in: Materials today : proceedings Vol. 57; pp. 768 - 774
• Main Authors: Mamo, Hana Beyene, Tura, Amanuel Diriba, Johnson Santhosh, A., Ashok, N., Kamalakara Rao, Dommeti
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 2022
• Subjects: 3D printing; Flexural strength; Fused deposition modeling; Fuzzy logic modeling; Fused deposition modeling; 3D printing; Flexural strength; Fuzzy logic modeling
• ISSN: 2214-7853; 2214-7853
• DOI: 10.1016/j.matpr.2022.02.306

•The link between input parameters and the flexural strength of the ABS printed components were analyzed.•Fused deposition modeling was considered in this study.•A Mamdani-type fuzzy logic model is developed to elucidate the operational and to predict the outputs.•The considered operational parameters are layer thickness, raster width, raster angle, and orientation angle. Fused Deposition Modeling (FDM) is an additive manufacturing technique used for prototype, physical modeling, and end-of-line production. Proper parameter selection is essential for producing high-quality items with suitable mechanical properties, such as flexural strength. Four critical process variables, such as layer thickness, raster width, raster angle, and orientation angle, are investigated in this study. The link between these factors and the flexural strength of the Acrylonitrile Butadiene Styrene (ABS) printed components are analysed using analysis of variance. The fuzzy logic (FL) approaches were used to develop a prediction model. The test samples are made on a 3D forge dreamer II printing machine of the FDM type. In Minitab software, the Taguchi mixed L18 orthogonal design technique was used to organize the trials. The result showed that the raster angle has the most significant parameter affecting flexural strength when compere with layer thickness, raster width and orientation angle. The fuzzy logic models provided high accuracy prediction of flexural strength with the maximum percentage error of 3.62%, and this revealed that the model and the experimental data were in good agreement.