Multi-objective Bayesian optimization of fused filament fabrication parameters for enhanced specific fracture energy in PLA-carbon fiber composites

Abstract

Short carbon fiber-reinforced polymers (SCFRPs) are used in automotive and aerospace applications because of their superior strength-to-weight ratio and resistance to fatigue and thermal stresses. Fused filament fabrication (FFF) is considered an efficient fabrication method for SCFRP components, yet optimizing process parameters for specific performance targets is non-trivial and resource consuming. Herein, a multi-objective Bayesian optimization (MOBO) framework for FFF-based SCFRP fabrication has been developed to fine-tune process parameters to optimize the trade-off between tensile strength and weight of the products. Initial random experiments were conducted on printed samples with different infill densities, printing speeds, and layer heights to evaluate their impact on specific tensile fracture energy (STFE) and component weight. These parameters were found to influence STFE and weight significantly. Data-driven sequential experimentation was then applied to identify a set of optimal values for these parameters. A surrogate-based optimization framework was utilized, and optimal points were determined through iterative refinement, with the process culminating when further iterations did not significantly enhance weight or STFE, indicating Pareto optimality. The efficacy of the methodology was demonstrated by identifying FFF parameters that achieve Pareto optimal STFE-to-weight ratios in nine iterations. This data-driven approach provides an efficient route to process optimization compared to heuristic and computational methods, streamlining the SCFRP design process via FFF and potentially extending to other composite systems and manufacturing processes.