Multi-objective optimization of PLA-FDM parameters for enhancement of industrial product mechanical performance based on GRA-RSM and BBD

The FDM™/FFF technology is widely adopted in both hobbyist and professional 3D printing communities, offering a plethora of printing parameters for customization. Optimizing the printing process to achieve desired mechanical properties provides a clear advantage. This research focuses on analyzing the elastic mechanical properties of PLA specimens manufactured through the FFF process. To minimize the number of experimental runs, a design of experiments approach, guided by a second-order equation-based response surface methodology, is employed to investigate the influence of four parameters: infill density, layer thickness, raster angles, and printing speed. Gray relational analysis is utilized as a multi-objective optimization tool for the analysis and optimization of mechanical tests. This will lead to the success manufacturing of a better specimen with comprehensive mechanical properties. The effects of these parameters on tensile and flexural strengths, as well as Young's modulus, are assessed by using analysis of variance. This statistical technique is employed to identify the significance and percent contribution of a particular process parameter on each mechanical property separately. The results indicate that the most influential factors are raster angles and infill density. The variable settings optimizing ultimate strength and Young's modulus responses simultaneously for tensile and flexural tests were identified using the response optimizer toolbox in Minitab 18^® software. The optimal parameter combination identified is a layer thickness of 0.1 mm, an infill density of 100%, a raster angle of 0°, and a printing speed of 40 mm/s. Furthermore, a validation process is conducted using standardized specimens to confirm the effectiveness of the optimal solution. The applicability of the outcomes is evaluated by comparing FDM-printed assembly components with an industrial mold-injected-backpack buckle. Further investigations might adopt a similar methodology, with the potential to expand the scope of considered factors to encompass a broader range applicable to various additive manufacturing technologies.