Study of Processing Parameters and Aging Treatments on Relative Density and Microhardness of Additively Manufactured Maraging Steel
Bibliographic record
Abstract
This research examined maraging steel (C300), which is widely used in the automotive industry. The study investigated how various 3D printing parameters—laser power (P), scanning speed (V), and layer spacing (H)—as well as post-processing heat treatment factors such as time (t) and temperature (T) affect the properties of C300 steel produced via selective laser melting (SLM). The primary properties assessed included relative density, porosity, hardness, and microstructure. The first part of the analysis focused on how processing parameters, time, and temperature influenced porosity types and manufacturing defects. Subsequently, ANOVA was employed to explore the sensitivity of relative density and microhardness to these parameters. The results revealed an optimal combination of parameters that improved both microstructural and mechanical properties. Additionally, the post-processing heat treatment was found to impact microhardness by modifying the microstructure and martensite lath size, with both the processing parameters and treatments affecting the material’s porosity and relative density.
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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 | 0.000 | 0.001 |
| 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".