Optimization of Process Parameters for Polylactic Acid (PLA) of FDM Using Particle Swarm Optimization (PSO)

Abstract

Nowadays, more industries across a wide range of sectors are embracing 3D printing because it offers several major benefits over more traditional production processes. However, there are still have issues which will increase the defects in 3D printing parts which may affect the precision of the product. Therefore, this study is carried out to investigate the process parameters which can affect the performance of FDM production and then optimize the selected process parameters by using the Particle Swarm Optimization (PSO) method. The material used in this study is Polylactic Acid (PLA) and the printed model is All-in-one 3D printing tester. The independent variable of this study are layer thickness, print speed, print temperature, retraction distance and infill percentage while the dependent variable is dimensional error. Multiple linear regression (MLR) is used to determine the influence of process parameters on all tests and dimensional accuracy. Besides, the dimensional error model was created and utilized in PSO. The result of minimization of dimensional error by PSO is successfully obtained which are 0.0805mm (layer thickness), 49.7568mm/s (print speed), 195.7164°C (print temperature), 0.8462mm (retraction distance) and 27.5899% (infill percentage). It is not the optimum process parameter value which can be proved by percentage error between the theoretical and experimental optimum process parameters values. The validation model is ranked at 17 out of 25 models which can be concluded that the optimized process parameter value from PSO is confirmed not the optimum value. In conclusion, the optimum process parameter values in this study will be taken from the experimental run model which has the lowest dimensional error, the process parameter values of X₃ are 0.05mm (layer thickness), 50mm/s (print speed), 210°C (print temperature), 1.5mm (retraction distance), and 25% (infill percentage).