Abstract
Polymers have a wide application. Fused deposition modeling (FDM) is a ubiquitous additive manufacturing (AM) technique that enables producers to manufacture polymer parts in a cost-effective way. However, the bed size limitation in 3D printers restricts their applicability in a variety of industries; therefore, it seems essential to find a great approach to join 3D-printed parts. Friction stir welding (FSW) is an excellent solution to join 3D-printed parts to overcome bed size limitation. A friction welding joint is referred as interface, fusion, or bond and is a point or edge where two or more pieces are joined together by friction. In the first stage, the aim of this investigation is to study the effect of groove shape and volume fraction of the multi-wall carbon nanotube powder on the mechanical properties including angular distortion, tensile and flexural strength, and hardness as well as electrical properties of ABS parts manufactured by fused deposition modeling machine. Secondly, the impact of those parameters on tensile strength via two artificial neural networks (ANN and GMDH) was investigated. The results showed that not only greater tensile and flexural strength was achieved thanks to the carbon nanotube, but also hardness was improved. The lower amount of powder and square groove shape leads to greater tensile and flexural strength. The square groove shape and more volume fraction of MWCNTS also gave rise to lower angular distortion. The positive influence of adding MWCNTs on joining 3d-printed parts via FSW was not restricted to mechanical properties and adding only 4.16% of MWCNTs showed a significant increase in electrical properties. Finally, both ANN and GMDH have shown excellent results (R-value of 0.94 and 0.93 with the error of 8 and 20%, respectively) in predicting the tensile strength of welded specimens.
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