Evaluation of Maraging Steel Produced Using Hybrid Additive/Subtractive Manufacturing
Bibliographic record
Abstract
Hybrid manufacturing is often used to describe a combination of additive and subtractive processes in the same build envelope. In this research study, hybrid manufacturing of 18Ni-300 maraging steel was investigated using a Matsuura LUMEX Avance-25 system that integrates metal additive manufacturing using laser powder bed fusion (LPBF) processing with high-speed machining. A series of benchmarking coupons were additively printed at four different power levels (160 W, 240 W, 320 W, 380 W) and with the integration of sequential machining passes after every 10 deposited layers, as well as final finishing of selected surfaces. Using non-contact three-dimensional laser scanning, inspection of the final geometry of the 18Ni-300 maraging steel coupons against the computer-aided design (CAD) model indicated the good capability of the Matsuura LUMEX Avance-25 system for net-shape manufacturing. Linear and areal roughness measurements of the surfaces showed average Ra/Sa values of 8.02–14.64 µm for the as-printed walls versus 0.32–0.80 µm for the machined walls/faces. Using Archimedes and helium (He) gas pycnometry methods, the part density was measured to be lowest for coupons produced at 160 W (relative density of 93.3–98.5%) relative to those at high power levels of 240 W to 380 W (relative density of 99.0–99.8%). This finding agreed well with the results of the porosity size distribution determined through X-ray micro-computed tomography (µCT). Evaluation of the static tensile properties indicated that the coupons manufactured at the lowest power of 160 W were ~30% lower in strength, 24% lower in stiffness, and more than 80% lower in ductility relative to higher power conditions (240 W to 380 W) due to the lower density at 160 W.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".