Surface functionalization of binder jetted steels through super-solidus liquid phase sintering and electro-spark deposition
Bibliographic record
Abstract
This study proposed a surface functionalization strategy for porous binder jet additive manufacturing (BJAM) steels. The methodology involves a sequential process of super-solidus liquid phase sintering (SLPS) followed by electro-spark deposition (ESD). SLPS with varying liquid fractions was used to modify the initial surface quality of BJAM steels, creating a range of surface roughness and bulk porosity. The ability of ESD to address substrate surface roughness and porosity variation was investigated using lower (Inconel 625) and higher (WC-Co) melting point coating materials. The results revealed a clear correlation between substrate surfaces and the surface finish, uniformity, and thickness of ESD deposits. Inconel 625, with its lower melting point, showcased an infiltration behavior and high tolerance to substrate conditions. Conversely, achieving high-quality WC-Co coating necessitated prior SLPS treatment to attain substrates with minimal roughness and sub-surface porosity. This investigation provides insights into optimizing surface modifications for porous BJAM metal parts, considering different coating materials and their compatibility with substrate conditions. • ESD was applied on binder jetted steels with various surface roughness and porosity. • SLPS of the substrate increased the deposition rate and uniformity of ESD deposits. • ESD Inconel 625 can fill substrate pores and tolerate high substrate roughness. • ESD WC-Co solidified on the substrate surface and was sensitive to substrate roughness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".