This study focuses on evaluating the effect of annealing time on the mechanical properties and structural changes in polyamide-based materials manufactured using fused filament fabrication (FFF) technology. Three materials were experimentally analysed: neat polyamide PA6, polyamide reinforced with 30% glass fibers (PA6 GF30), and the composite material Onyx. After fabrication, the test specimens were annealed at a temperature of 180 °C for 30, 60, and 100 min. Mechanical properties were evaluated by tensile testing in accordance with ISO 527, and the obtained data were further processed using machine learning methods (Linear SVM, Quadratic SVM, and K-NN) to classify individual levels of thermal exposure. The results showed that annealing significantly improved the tensile strength of Onyx from 50.78 ± 1.46 MPa (0 min) to 60.09 ± 1.30 MPa after 30 min, corresponding to an increase of approximately 18%, while further annealing (60 and 100 min) resulted in values between 59.23 and 62.12 MPa without statistically significant additional improvement. In contrast, PA6 GF30 exhibited a progressive decrease in tensile strength from 76.85 ± 0.87 MPa (0 min) to 51.91 ± 8.03 MPa after 100 min, representing an overall reduction of approximately 32%, indicating degradation of the polymer–fiber interface. For neat PA6, tensile strength decreased from 55.31 ± 3.83 MPa to 40.03 ± 9.36 MPa, but these differences were not statistically significant (p > 0.05). Machine learning classification confirmed predominantly linear material behavior, with Linear SVM achieving accuracies of 85% for Onyx and PA6, and 95% for PA6 GF30, outperforming Quadratic SVM and K-NN models. These findings provide valuable insights for optimizing post-processing conditions of FFF-manufactured polyamide materials and composites.
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Lucia Boszorádová
Martin Baráth
Martin Kotus
Applied Sciences
VSB - Technical University of Ostrava
Slovak University of Agriculture in Nitra
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Boszorádová et al. (Mon,) studied this question.
www.synapsesocial.com/papers/69df2ae6e4eeef8a2a6afe32 — DOI: https://doi.org/10.3390/app16083791
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