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<p>This study introduces a novel AI-based prediction framework for Fused Filament Fabrication (FFF) process optimization, integrating high-fidelity simulation with machine learning and design of experiments (DOE) analysis. In the proposed approach, a full-factorial DOE matrix is employed to drive Digimat-AM, a physics-based thermo-mechanical simulation tool, generating an exhaustive dataset to train six machine learning models (Random Forest, XGBoost, ANN, SVR, AdaBoost, and KNN). These models predict four critical responses: deflection, residual stress, print time, and shape tolerance (dimensional accuracy), and their performance is benchmarked against classical DOE response-surface models. The AI models exhibit outstanding predictive accuracy: ensemble methods (Random Forest, XGBoost) achieved near-perfect agreement with simulation outputs, significantly outperforming DOE models. Interpretability is ensured via 3D response-surface plots, which illustrate the influence of parameter interactions and align with known thermo-mechanical trends. This combination of predictive fidelity and transparent analysis delivers valuable design insights and practical utility. The trained ML models serve as fast surrogates (digital twins), enabling rapid “what-if” scenario exploration and reducing reliance on trial-and-error in print optimization. This AI-driven framework streamlines additive manufacturing workflows by accelerating process tuning and laying the foundation for intelligent, data-driven FFF optimization.</p>

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Accelerating fused filament fabrication FFF optimization with AI-Powered digital twins and High-Fidelity simulations

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月15日

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  • Baha Eddine Ben Brayek,
  • Youssef Qarssis,
  • Abdellatif Ghennioui,
  • Abdelkhalak El. Hami,
  • Mostapha Tarfaoui

摘要

This study introduces a novel AI-based prediction framework for Fused Filament Fabrication (FFF) process optimization, integrating high-fidelity simulation with machine learning and design of experiments (DOE) analysis. In the proposed approach, a full-factorial DOE matrix is employed to drive Digimat-AM, a physics-based thermo-mechanical simulation tool, generating an exhaustive dataset to train six machine learning models (Random Forest, XGBoost, ANN, SVR, AdaBoost, and KNN). These models predict four critical responses: deflection, residual stress, print time, and shape tolerance (dimensional accuracy), and their performance is benchmarked against classical DOE response-surface models. The AI models exhibit outstanding predictive accuracy: ensemble methods (Random Forest, XGBoost) achieved near-perfect agreement with simulation outputs, significantly outperforming DOE models. Interpretability is ensured via 3D response-surface plots, which illustrate the influence of parameter interactions and align with known thermo-mechanical trends. This combination of predictive fidelity and transparent analysis delivers valuable design insights and practical utility. The trained ML models serve as fast surrogates (digital twins), enabling rapid “what-if” scenario exploration and reducing reliance on trial-and-error in print optimization. This AI-driven framework streamlines additive manufacturing workflows by accelerating process tuning and laying the foundation for intelligent, data-driven FFF optimization.