Heat Treatment of Multi-Material Additively Manufactured Maraging Steel and Co-Cr-Mo Alloy
Bibliographic record
Abstract
The prospect of converting an entire assembly of parts with challenging geometry to a single part with sectional variation of properties has stimulated a growing interest in multi-material Additive Manufacturing (AM). Accordingly, the present work utilized a dual-metal Laser Powder Bed Fusion (LPBF) technique to manufacture a multi-material component, consisting of Co-Cr-Mo alloy (MP1) and maraging steel (MS1) in a single manufacturing process. The research also attempted to establish a heat treatment strategy compatible with these alloys. The resulting heat treatment effects on the microstructure, texture, and microhardness were investigated. Diffusion calculation results suggested an overall diffusion depth of 120 μm in the interface after heat treatment, which can increase the resulting joint strength if intermetallic precipitation is avoided. Electron Backscatter Diffraction (EBSD) analysis of the heat-treated samples showed that both the base metal regions retained the dominant fiber textures after printing, which is the <110> || building direction (BD) fiber texture for the MP1 region and the <111> || BD and <100> || BD fiber textures for the MS1 region. Nanoindentation tests also revealed a considerably higher hardness in the MS1 region and a slight reduction of hardness in the MP1 region after heat treatment, which can be early evidence of the successful application of the heat treatment strategy to both base metals. Future work will investigate the mechanical properties of the as-printed and heat-treated samples and verify if any precipitates formed in the MS1-MP1 interface.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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 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".