abstract

The study is based on the multi-objective optimization of the 3D printed parts created by Fused Deposition Modeling (FDM), the process focused on enhancing mechanical properties, dimensional performance, and surface quality. The experiment uses hybrid optimization method which combines Taguchi experimental design, Grey Relational Analysis (GRA) and Weibull reliability modeling in order to come up with the best combination of the process parameters. PLA, ABS, and ceramic reinforced PLA were evaluated as thermoplastic materials in order to assess their capability of fulfilling various performance goals. The findings reveal that the combination of statistical and computational optimization methods can be successfully used to improve the overall level of quality and uniformity of the components produced by FDM. The paper concludes that hybrid multi-objective optimization offers a strong and methodical paradigm to enhance the trustworthiness and effectiveness of additive manufacturing systems used in all types of engineering processes.