Benchmarking of 316L Stainless Steel Manufactured by a Hybrid Additive/Subtractive Technology
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Bibliographic record
Abstract
This research study investigated the hybrid processing of 316L stainless steel using laser powder bed (LPB) processing with high-speed machining in the same build envelope. Benchmarking at four laser powers (160 W, 240 W, 320 W, and 380 W) was undertaken by building additively with machining passes integrated sequentially after every ten deposited layers, followed by the final finishing of select surfaces. The final geometry was inspected against the computer-aided design (CAD) model and showed deviations smaller than 280 µm for the as-built and machined surfaces, which demonstrate the good efficacy of hybrid processing for the net-shape manufacturing of stainless steel products. The arithmetic average roughness values for the printed surfaces, Ra (linear) and Sa (surface), were 11.4 um and 14.9 um, respectively. On the other hand, the vertical and horizontal machined surfaces had considerably lower roughness, with Ra and Sa values ranging between 0.33 µm and 0.70 µm. The 160 W coupon contained layered, interconnected lack of fusion defects which affected the density (7.84 g·cm−3), yield strength (494 MPa), ultimate tensile strength (604 MPa), Young’s modulus (175 GPa), and elongation at break (17.3%). By contrast, at higher laser powers, near-full density was obtained for the 240 W (7.96 g·cm−3), 320 W (7.94 g·cm−3), and 380 W (7.92 g·cm−3) conditions. This, combined with the isolated nature of the small pores, led to the tensile properties surpassing the requirements stipulated in ASTM F3184—16 for 316L stainless steel.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it