3D Printing of Wear-Resistant Materials: A Review and Perspectives in Oil Sands Applications
Bibliographic record
Abstract
ABSTRACT Additive manufacturing (AM) has received attention from the oil sands industry in recent years. Oil sands producers use AM technologies to fabricate parts with complex geometries that are difficult or impossible to make via conventional manufacturing. AM can also enable rapid iteration of design ideas and printing of prototypes directly from 3D CAD models quickly and cost-effectively. 3D printed cemented carbide and other wear-resistant materials have been investigated to improve operational reliability and reduce production costs. As AM is still in the early development stages, oil sands producers need to understand the capabilities and limitations of the various 3D printing processes available, comprehensively identify and validate suitable applications for their use. This paper reviews the current 3D printing technologies for making cemented carbide wear-resistant materials and emphasizes the potential applications, restrictions, and challenges of 3D printing applications in the oil sands industry.
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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 | 0.000 | 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 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".