Abstract Fused filament fabrication (FFF) is a widely adopted additive manufacturing technology whose process performance emerges from complex and often non-intuitive interactions among printing parameters, thermal conditions, material deposition mechanisms, and resulting structural properties. Despite its growing adoption in industrial and engineering contexts, identifying robust parameter combinations that simultaneously balance productivity, cost, surface quality, and mechanical performance remains a challenging task, particularly for fiber-reinforced polymer composites. In this study, a data-driven and explainable framework is proposed for the design and optimization of the PET-CF15 FFF process by integrating machine learning, Bayesian hyperparameter optimization, explainable artificial intelligence, and multi-objective metaheuristic optimization. More than twenty machine learning model–hyperparameter configurations are systematically evaluated to construct surrogate models capable of capturing both deterministic and stochastic manufacturing responses. Model interpretability is explicitly addressed through Shapley value analysis, enabling physical insight into the role of key printing parameters. The trained surrogates are embedded into a multi-objective optimization framework based on the Multi-objective Lichtenberg Algorithm, allowing the systematic exploration of competing and synergistic process objectives without additional experimental effort. Notably, the results reveal that, depending on the explored design space, objectives commonly assumed to be conflicting may converge toward compatible optima, highlighting the existence of stable operating regimes that are not evident from empirical or single-objective analyses. By unifying predictive accuracy, explainability, and multi-objective decision-making, the proposed approach provides a robust computational tool to support informed and efficient FFF process design for high-performance composite materials.
Cesário et al. (Tue,) studied this question.