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Experimental Investigation and Prediction of Mechanical Properties in a Fused Deposition Modeling Process

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dc.contributor.author Tura, Amanuel Diriba
dc.contributor.author Lemu, Hirpa G
dc.contributor.author Mamo, Hana Beyene
dc.date.accessioned 2023-11-03T12:03:11Z
dc.date.available 2023-11-03T12:03:11Z
dc.date.issued 2022-06-20
dc.identifier.uri https://repository.ju.edu.et//handle/123456789/8761
dc.description.abstract Additive manufacturing, also known as three‐dimensional printing, is a computer‐ controlled advanced manufacturing process that produces three‐dimensional items by depositing materials directly from a computer‐aided design model, usually in layers. Due to its capacity to manufacture complicated objects utilizing a wide range of materials with outstanding mechanical qualities, fused deposition modeling is one of the most commonly used additive manufacturing technologies. For printing high‐quality components with appropriate mechanical qualities, such as tensile strength and flexural strength, the selection of adequate processing parameters is critical. Experimentally, the influence of process parameters such as the raster angle, printing orientation, air gap, raster width, and layer height on the tensile strength of fused deposition modeling printed items was examined in this work. Through analysis of variance, the impact of each parameter was measured and rated. The system’s response was predicted using an adaptive neuro‐fuzzy technique and an artificial neural network. In Minitab software, the Box‐Behnken response surface experimental design was used to generate 46 experimental trials, which were then printed using acrylonitrile butadiene styrene polymer materials on a three‐dimensional forge dreamer II fused deposition modelling printing machine. The results revealed that the raster angle, air gap, and raster width had significant impacts on the tensile strength. The adaptive neuro‐fuzzy approach and artificial neural network predicted tensile strength accurately with an average percentage error of 0.0163 percent and 1.6437 percent, respectively. According to the findings, the model and experimental data are in good agreement. en_US
dc.language.iso en_US en_US
dc.publisher MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. en_US
dc.subject fused deposition modeling en_US
dc.subject mechanical properties en_US
dc.subject tensile strength en_US
dc.subject adaptive neuron‐fuzzy methods en_US
dc.subject artificial neural network en_US
dc.title Experimental Investigation and Prediction of Mechanical Properties in a Fused Deposition Modeling Process en_US
dc.type Article en_US

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