Effect of thermal history on the fracture and fatigue behaviors of semi-crystalline polymers prepared via material extrusion additive manufacturing
Bibliographic record
Abstract
• Fatigue life for material extrusion additive manufacturing (MEX-AM) was explored. • Lower nozzle temperatures in MEX-AM lead to rapid, brittle crack growth. • Increased interfacial temperatures in MEX-AM lead to greater fatigue life. • Increased temperature in MEX-AM reduced void coalescence ahead of the crack tip. • Increasing temperature increased plasticity at the crack tip, enhancing fracture energy dissipation. Material extrusion (MEX) additive manufacturing (AM) is transforming the design and production of complex structures, providing reliable on-demand components. However, the effect of thermal history on the resultant microstructure and damage tolerance of MEX-AM materials is not fully understood. This research investigates the critical role of interfacial thermal history, which is dependent on processing conditions, in determining the fracture and fatigue behaviors of semi-crystalline polymers, as exemplified by polyamide-6 (PA-6). Utilizing infrared thermography, the thermal history, and its dependence on nozzle temperature of extruded PA-6, was investigated. Quasi-static and cyclic tests of compact tension specimens were used to evaluate fracture and fatigue performance. The K IC in samples produced at a nozzle temperature of 260 °C were 201% and 18% higher than those fabricated at 240 °C and 280 °C, respectively. X-ray computed tomography showed thermal history significantly influences interfacial diffusion and void content, directly affecting performance. Optical microscopy and digital image correlation identified damage mechanisms and examined strain evolution around crack tips, revealing that interfacial thermal history governed crack tip plasticity, impacting the energy release rate. This study establishes a crucial process-structure–property-performance relationship and highlights the damage tolerance of MEX-AM polymers, showcasing their potential for advanced structural applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".