Experimental multi-objective optimization of 3D printing parameters for enhanced energy absorption in MCC/PC-ABS composites using MOGA-II algorithm

The increasing demand for lightweight and sustainable materials has driven the development of bio-reinforced polymer composites for advanced engineering applications. However, the dynamic mechanical performance of fused Filament Fabrication (FFF) fabricated composites is strongly influenced by processing parameters, making the identification of optimal printing conditions a significant challenge. This study aims to optimize the dynamic mechanical properties of microcrystalline cellulose (MCC)-reinforced polycarbonate/acrylonitrile butadiene styrene (PC/ABS) composites through a multi-objective optimization approach. MCC/PC-ABS composites were fabricated using FFF and characterized using dynamic mechanical analysis (DMA). Build orientation, air gap, and layer height were selected as the key process parameters. Regression models were developed from experimental data and integrated with a MOGA-II evolutionary algorithm to minimize the damping factor while maintaining desirable storage modulus and loss modulus values. The optimal processing conditions were identified as a 45° build orientation, 0.21 mm air gap, and 0.2 mm layer height. Under these conditions, the storage modulus increased by 78% and the loss modulus decreased by 66%, resulting in a reduced damping factor. Microstructural observations further confirmed the influence of processing parameters on the dynamic mechanical behavior of the composites. These findings demonstrate the effectiveness of MOGA-II-based optimization for tailoring the performance of MCC/PC-ABS composites for energy-absorbing applications.