A Low-Viscosity, Recyclable Polymer-Based Binder Strategy for Metal FDM: Toward High Powder Loading, Sustainable Processing, and Comprehensive Characterization of 17-4PH Stainless Steel Parts
Bibliographic record
Abstract
In metal fused deposition modeling (FDM), performance is governed by feedstock formulation, most critically the metal solid loading, while binder selection is constrained by environmental impacts and limited recyclability. This study investigates the development and performance of highly filled 17-4PH stainless steel (17-4PH) feedstocks formulated with a low-molecular-weight polymer binder system, specifically designed for FDM in metal additive manufacturing (AM). The binder system, composed of low-cost, recyclable paraffin wax and stearic acid, was used to prepare feedstocks containing 93.0–96.0 wt.% metal powder. Rheological analysis indicated that intermediate powder loadings (95.0–95.5 wt.%) yielded optimal shear-thinning behavior, essential for stable extrusion during printing. Printing trials identified 95.5 wt.% as the critical powder loading, delivering superior print fidelity and structural integrity relative to both under-filled (93.0–94.5 wt.%) and overfilled formulations. Green part characterization revealed increased density and flexural modulus with rising powder content, while thermal debinding and sintering trials indicated enhanced thermal stability and dimensional retention at higher loadings. The as-sintered specimens from the 95.5 wt.% feedstock achieved a relative density (RD) of 96.5% and significantly improved mechanical performance, including an ultimate tensile strength (UTS) of 758 MPa and 5.2% elongation, clearly outperforming the 95.0 wt.% variant. Tribocorrosion testing further validated these improvements, with the higher-density samples showing a lower coefficient of friction and a reduced wear coefficient of 2.1 × 10−5 mm3·(N·m)−1 in 3.5% NaCl solution.
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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) | 0.000 | 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".