Influence of Intentionally Induced Porosity and Postprocessing Conditions on the Mechanical Properties of Laser Powder Bed–Fused Inconel 625
Bibliographic record
Abstract
Additive manufacturing (AM) technologies, such as laser powder bed fusion (LPBF), have gained significant attention because of their capacity to manufacture near-net shape complex metallic components. Although LPBF components can manifest static mechanical properties that are comparable to those of their wrought counterparts, processing-induced defects, such as porosity and lack of fusion, are regularly observed within the build and are of particular concern for the structural integrity of printed components. In this work, the impact of LPBF-induced defects on the static mechanical properties of Inconel 625 specimens is studied. To establish the relationship between the level of such defects and the specific combinations of LPBF parameters, coupons with porosities of up to 20% were manufactured by varying the laser power from 70 to 360 W, the scanning speed from 720 to 3840 mm/s, and the hatching space from 0.08 to 0.33 mm (a constant layer thickness of 40 microns was used). To measure the level of processing-induced porosity, the computed microtomography (micro-CT) and Archimedes' techniques were concurrently applied. The micro-CT also was used to evaluate the nature and morphology of defects and their distributions, resulting from different combinations of processing parameters. Next, tensile specimens with porosities of up to 3% with two build orientations (0° and 90°) were manufactured and subjected to stress relief annealing and hot isostatic pressing. The specimens then were tested to measure the impact of the prosessing-induced porosity, build orientation, and postprocessing conditions on the static mechanical properties of Inconel 625 specimens. Our results indicated that the presence of pores strongly reduces the ductility of the material, especially when stresses are applied along the build direction. Although hot isostatic pressing allowed a significant reduction in porosity, this postprocessing was ineffective in improving the ductility of specimens with as-printed porosities exceeding 0.3%.
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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) | 0.000 | 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 teacher head, 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".