The Analysis and Control of Distortion in Carbonitrided and Nitrocarburized Thin-Shelled Plain Carbon Steel Automotive Powertrain Components
Bibliographic record
Abstract
Abstract Gaseous ferritic nitrocarburizing was investigated as a replacement for the carbonitriding process currently used to improve the surface properties of thin-shelled, plain carbon steel automotive powertrain components. Nitrocarburizing can impart a hard, wear-resistant case suitable for light load applications at lower processing temperatures than carbonitriding, thereby potentially reducing size and shape distortions and the associated need for finish grinding. Carbonitriding and gaseous ferritic nitrocarburizing were compared with respect to size and shape distortion, retained austenite, and residual stress values. Testing was performed on a torque converter piston, an automotive powertrain component stamped from a sheet of 3-mm cold-worked SAE 1010 steel. A total of 53 pistons were used. While the heat treatment conditions for the carbonitrided pistons were similar to the current production schedule, conditions for the nitrocarburized pistons incorporated a range of processing times from 2–14 hours and temperatures from 510–605°C (950–1125°F). The results of this work were generally consistent with those from an earlier study involving the use of Navy C-ring specimens. For both the Navy C-rings and torque converter pistons, the nitrocarburizing process gave rise to lower part distortion, but resulted in overall lower compressive residual stress values than in the carbonitrided steel.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".