Laser powder bed fusion of difficult-to-print γ′ Ni-based superalloys: A review of processing approaches, properties, and remaining challenges
Bibliographic record
Abstract
Metal additive manufacturing (AM) promises a revolution with the potential to fabricate more complex, lighter, and higher performance components while simplifying supply chains and reducing material waste. However, many of the superalloys that are critical to applications requiring superior high-temperature properties are also challenging to process using fusion-based AM. The number of publications on this topic has grown significantly in the past five years, reflecting a growing interest within industry and academia. This article reviews and discusses the challenges associated with powder bed fusion - laser beam (PBF-LB) of γ′ Ni-based superalloys and recent approaches that have enabled their processing. This includes process parameter optimization, alloy modification, and heat treatment, all of which have been shown to produce material with reduced defect density. Additionally, the properties of γ′ Ni-based superalloys made with PBF-LB and conventional (cast and wrought) processes are compared and the reasons for the observed differences are discussed. Current and future research trends are provided based on the current outstanding challenges and prevailing theories in the literature, as well as an outlook on the adoption of PBF-LB γ′ Ni-based superalloys in industry.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".