Material extrusion additive manufacturing of aluminum-based feedstocks
Bibliographic record
Abstract
AlSi10Mg is a versatile aluminum alloy widely used in the aerospace and automotive industries due to its high strength-to-weight ratio, corrosion resistance, thermal conductivity, and machinability. It is also one of the most popular aluminum alloys for additive manufacturing (AM) via laser powder bed fusion (LPBF) processes. In recent years, AM via material extrusion (MEX) has been adapted to produce metallic parts using powder-polymer feedstocks similar to those employed in metal injection molding (MIM). This approach has demonstrated suitability for fabricating complex geometries with good dimensional accuracy. However, the fabrication of aluminum parts via MEX remains largely undemonstrated. This study aims to investigate the feasibility of using AlSi10Mg powders in low-viscosity MIM-like feedstocks for manufacturing high-density parts through the MEX process. Feedstocks containing 65 vol. % of AlSi10Mg powder, 2 vol. % of stearic acid, 8 vol. % of ethylene-vinyl acetate, and 25 vol. % of paraffin wax were tested. Three different powder types were evaluated: a MIM-grade powder (0-20 ?m), a binder jetting powder (20-63 ?m), and a mixed powder (0-110 ?m). The particle size distribution of the AlSi10Mg powder was identified as a critical factor for successful printing. The MIM-grade powder resulted in over-extrusion defects, while the mixed powder yielded parts with minimal surface defects. The binder jetting powder was not printable due to binder segregation. Thermal wick-debinding was performed on simple printed geometries at 250°C for 2 hours under an industrial-grade argon atmosphere. Liquid-phase sintering was conducted on loose powders and debound parts in a high-purity nitrogen atmosphere at 575°C for 2 hours, with magnesium chips used as an oxygen getter to mitigate oxidation. The sintered powders exhibited a dense and homogenous microstructure, achieving up to 98% of the theoretical density, as measured using the Archimedes’ method. Sintered printed geometries demonstrated a similar microstructure but experienced warping and cracking, highlighting the need for further optimization of the sintering process for MEX parts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.055 | 0.005 |
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; both teacher heads agree on what is shown here.
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".