OPTIMIZATION OF PROCESS PARAMETERS OF 3D PRINTED POLYLACTIC ACID PARTS USING GENETIC ALGORITHM (ASEAN Engineering Journal, 2025)
In this paper, the optimization of input process parameters of 3D printed parts of Polylactic Acid in Fused Deposition Modeling is studied using a genetic algorithm. The work emphasizes on influence of input process parameters on Young’s modulus, breakage tension, and breakage deformation. Optimization of process parameters is studied for single objectives such as Young's modulus, break in tension, and breakage deformation and also optimizing combined three objective functions simultaneously. The regression model is used as a fitness function to optimize the objectives. Built-in functions in MATLAB ‘ga’ and ‘gamultiobj’ are applied for optimization of single and multi-objectives respectively. The study reveals that simultaneous optimization of three objectives reduces a 27.27% breakage deformation, 13.7% Young’s modulus, and 55.5% break-in tension as compared to optimization of individual objectives.