The additive manufacturing (AM) process is used to build three‐dimensional (3D)‐printed components by depositing layers of materials one over another until it completes the building process. There are numerous ways to improve its strength and rigidity of AM processed components. In this research, we discuss an effective method to improve the strength of 3D‐printed component joints by reinforcing carbon fiber (CF) with polylactic acid (PLA) and applying bioinspired interlock sutures. The strength of 3D‐printed components is highly influenced by build parameters such as printing speed, extruder temperature, and layer thickness. In this context, the design of experiment (DOE) was made to ensure the effect of build parameters. The specimens were fabricated according to DOE in order to characterize bending behavior, which was analyzed using special fixture. The bending strength and fracture behavior of specimen for three different interlock joints were studied. The analytical technique of particle swarm optimization was adopted to predict the optimum printing parameters to improve the bending strength of the 3D‐printed components. The result shows that CF‐reinforced PLA with spline interlock suture increases the bending strength of the 3D‐printed specimen when compared to pure PLA.
The most common method to fabricate both simple and complex structures in the additive manufacturing process is fused deposition modeling (FDM). Many researchers have studied the strengthening of FDM components by adding short carbon fibers (CF) or by reinforcing solid carbon fiber rods. In the current research, we sought to enhance the mechanical properties of FDM components by adding bioinspired solid CF rods during the fabrication process. An effective bonding interface of bioinspired CF rods and polylactic acid (PLA) was achieved by triangular interlocking sutures and by employing synthetic glue as the binding agent. In particular, the tensile strength of solid CF rod reinforced PLA samples was studied. Critical parameters such as layer thickness, extruder temperature, extruder speed, and shell thickness were considered for optimization. Significant process parameters were identified through leverage plots using the response surface methodology (RSM). The optimum parameters were found to be layer thickness of 0.04 mm, extruder temperature of 215 °C, extruder speed of 60 mm/s, and shell thickness of 1.2 mm. The results revealed that the bioinspired solid CF rod reinforced PLA (CFRPLA) composite exhibited a tensile strength of 82.06 MPa, which was approximately three times higher than the pure PLA (28 MPa, 66% lower than CFRPLA), acrylonitrile butadiene styrene (ABS) (28 MPa, 66% lower than CFRPLA), polyethylene terephthalate glycol (PETG) (34 MPa, 60% lower than CFRPLA), and nylon (34 MPa, 60% lower than CFRPLA) samples.
In this study, fused filament fabrication (FFF) printing parameters were optimized to improve the surface quality and reduce the printing time of Acrylonitrile Butadiene Styrene (ABS) polymer using the Analysis of Variance (ANOVA), it is a statistical analysis tool. A multi-objective optimization technique was employed to predict the optimum process parameter values using particle swarm optimization (PSO) and response surface methodology (RSM) techniques. Printing time and surface roughness were analyzed as a function of layer thickness, printing speed and nozzle temperature. A central composite design was preferred by employing the RSM method, and experiments were carried out as per the design of experiments (DoE). To understand the relationship between the identified input parameters and the output responses, several mathematical models were developed. After validating the accuracy of the developed regression model, these models were then coupled with PSO and RSM to predict the optimum parameter values. Moreover, the weighted aggregated sum product assessment (WASPAS) ranking method was employed to compare the RSM and PSO to identify the best optimization technique. WASPAS ranking method shows PSO has finer optimal values [printing speed of 125.6 mm/sec, nozzle temperature of 221 °C and layer thickness of 0.29 mm] than the RSM method. The optimum values were compared with the experimental results. Predicted parameter values through the PSO method showed high surface quality for the type of the surfaces, i.e., the surface roughness value of flat upper and down surfaces is approximately 3.92 µm, and this value for the other surfaces is lower, which is approximately 1.78 µm, at a minimum printing time of 24 min.
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