Quasi-Static and Dynamic Mechanical Response of Alloy 625 Fabricated Using Laser Powder Bed Fusion
Bibliographic record
Abstract
Additive manufacturing can provide advantages over conventional manufacturing for alloys such as alloy 625, which is expensive and difficult to machine. Laser powder bed fusion is a type of additive manufacturing that provides advantages but introduces complex effects on mechanical properties in produced components. This work examines some of these effects by assessing laser powder bed fusion processing parameters, several heat treatment schedules, and differing strain rate and temperature testing behavior using mechanical testing. It was determined that the porosity of fabricated samples of alloy 625 could be reduced below the control of 0.43 %, though the hardness does not appear to be sensitive to processing parameters. Heat treatments at higher temperatures appear to maintain a similar hardness to as-printed samples, but a treatment at 670 °C increased the hardness from 28.0 to 31.3 HRC. In compression tests, samples had higher stress/strain ratios in the dynamic range, though they did not fracture in any tests conducted. In a range from 25 to 500 °C, samples displaced a consistent thermal softening effect, suggesting that significant microstructural change may not occur, compatible with the typical high temperature working conditions of the alloy.
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) | 0.002 | 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".