Optimization of Fused Deposition Modeling Parameters for Mechanical Properties of Polylactic Acid Parts Based on Kriging and Cuckoo Search

As an emerging rapid manufacturing technology, 3D printing has been widely applied in numerous fields such as aerospace, shipbuilding, and wind power, by virtue of its advantage in efficiently fabricating components with complex structures and integrated functions. In response to the problems of poor mechanical properties and difficulty in selecting process parameters for fused deposition modeling (FDM), this paper analyzed the principle of FDM and proposed a parameter optimization method based on a Kriging and Cuckoo Search (CS) algorithm aimed at improving the mechanical properties of 3D printed polylactic acid (PLA) parts. Firstly, by analyzing FDM principle and its main parameters, printing speed and temperature were selected as research elements, and tensile strength as the mechanical performance index. Latin hypercube sampling (LHS) was integrated to generate a limited experimental sample set. Secondly, a Kriging-based prediction model for mechanical properties was constructed by learning sample data, and the nonlinear mapping relationship between process parameters and tensile strength was obtained. Then, using the combinations of speed and temperature as design variables and maximizing tensile strength as the optimization objective, an optimization model was established, and the optimal process parameters were searched by CS. The optimal printing velocity was 31 mm/s and printing temperature was 225 °C, and the corresponding maximum tensile strength was 38.27 MPa. Finally, compared to the test data, the relative prediction error of Kriging model was 0.62%, and the optimal strength (38.27 MPa) increased by about 12.7% compared to the average value (33.97 MPa) of experimental data. It can be seen that the Kriging model is effective, and the tensile strength of parts printed under the optimal process parameters is significantly improved.