This study introduces a data‑driven method for multi‑objective optimization in fused filament fabrication (FFF), targeting both geometric accuracy and filament bond quality. Experiments supply part‑quality data, which feed Bayesian neural network models that predict the two objectives while accounting for epistemic and aleatory uncertainties. Using these stochastic predictions, robustness‑based optimization explores nozzle temperature, speed, and layer thickness, producing Pareto surfaces that reveal trade‑offs and are validated through actual part manufacturing.
By Berkcan Kapusuzoglu, Paromita Nath, Matthew Sato, Sankaran Mahadevan, Paul Witherell
The article explores physics-informed and hybrid machine learning approaches for predicting bond quality and porosity in fused filament fabrication (FFF) parts. It examines three strategies—embedding physics constraints in the loss function, adding physics model outputs as inputs, and pre‑training with physics data—to enforce consistency with physical laws. Eight combinations of these strategies are tested, showing that integrating multiple approaches yields accurate predictions even with limited experimental data.
By Berkcan Kapusuzoglu, Sankaran Mahadevan
The paper presents a geometry‑informed modeling framework that couples a deep‑learning filament shape predictor (ShapeGen3DCP) with a layer‑activation finite element method to assess buildability in 3D concrete printing. By generating realistic filament geometries directly from material and process parameters, the approach eliminates the need for experimental filament characterization or fluid‑flow simulations. Validation and parametric studies show that extrusion parameters and filament shape significantly affect buildability predictions, especially for free‑flow deposition, and that an elliptical approximation balances fidelity and simplicity while volume‑conserving rectangular representations improve prediction reliability.
By Giacomo Rizzieri, Saif-Ur-Rehman, J\"org F. Unger, Annika Robens-Radermacher
arXiv:2606. 19375v1 Announce Type: new Abstract: Identifying anisotropic yield functions remains challenging since yielding is not directly observed in full-field mechanical measurements, directional calibration can require many loading directions, and selecting an appropriate analytical form is nontrivial.
By Hyeonbin Moon, Donghyuk Cho, Jecheon Yu, Jeong Whan Yoon, Seunghwa Ryu
arXiv:2602. 09120v2 Announce Type: replace Abstract: Electrospinning is a powerful technique for producing micro to nanoscale fibers with application specific architectures.
By Elisa Roldan, Tasneem Sabir
arXiv:2604. 14562v2 Announce Type: replace Abstract: Accurate temperature field prediction in metal additive manufacturing (AM) is essential for understanding the process-structure-performance relationship.
By Hyeonsu Lee, Jihoon Jeong