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Go to Editorial ManagerSpecimens with the structure of a face-centered cubic were produced using several sets of printing conditions. An experimental testing is conducted to carefully evaluate the microstructural analysis and compressive strength of this structure. The results include the measurement of mechanical properties, such as the peak stress. Fused deposition modeling is employed for the additive manufacturing of experimental specimens made from shape memory polymer thermoplastic polyurethane (MM-3520). We take into account the impact of printing factors on lattice structures, such as layer thickness, printing temperature, and printing speed. Analyzing the microstructure of the printed specimens exhibits that the specimens with highest printing temperature, lowest printing speed and thinner printing layer have better layers adhesion and lower porosities. All the mechanical tests are performed on specimens with the same structure and at a relatively constant density. Among the tested printing parameters, using a layer height of 0.1 mm, a printing temperature of 230 °C, and a printing speed of 20 mm/s yields the highest strength in the specimens. However, specimens printed with a layer height of 0.2 mm, a printing temperature of 220 °C, and a printing speed of 30 mm/s also exhibit good strength, albeit slightly lower than the maximum values. Additionally, when using these specific settings (0.3 mm – 210 °C – 40 mm/s), the mechanical qualities are minimized, yet the stress-strain curves exhibit characteristics similar to elastomers.
Fused deposition modeling (FDM) is a commonly used 3D printing technique that involves heating, extruding, and depositing thermoplastic polymer filaments. The quality of FDM components is greatly influenced by the chosen processing settings. In this study, the Taguchi technique and artificial neural network were employed to predict the ultimate tensile strength of FDM components and establish a mathematical model. The mechanical properties of ABS were analyzed by varying parameters such as layer thickness, printing speed, direction angle, number of parameters, and nozzle temperature at five different levels. FDM 3D printers were used to fabricate samples for testing, following the ASTM-D638 standards, using the Taguchi orthogonal array experimental design method to set the process parameters. The results indicated that the printing process factors had a significant impact on tensile strength, with test values ranging from 31 to 38 MPa. The neural network achieved a maximum error of 5.518% when predicting tensile strength values, while the analytical model exhibited an error of 19.376%.