Optimized Surface Roughening by Pulsed Waterjet for Suitable Adhesion Strength of Plasma Transferred Wire Arc Coating
Bibliographic record
Abstract
This study utilized a high-pressure pulsed waterjet process and paired it with the plasma transferred wire arc technology to develop a novel technique to remanufacture worn-out engine cylinder bores and give the engine new life.The plasma transferred wire arc technology is currently being used for the engine remanufacturing process by major auto-industries to deposit the wear resistant top-coat.One of the steps during the remanufacturing process is to chemically deposit a nickel-aluminum pre-bond coat for a better top-coat bond strength.The idea behind this project is to eliminate the need for the expensive pre-bond coat step by optimizing the surface roughness profile of the substrate to provide acceptable mechanical bonding between the coating and the substrate.The desired outcome is a coating-substrate adhesion strength greater than 30 MPa, which is required for engine cylinder bore liners application.In this study, low carbon stainless-steel was plasma spray coated on a wide range of pulsed waterjet roughened surface profiles generated on grey cast iron and cast aluminum A380 alloy, two commonly used engine materials.The roughened surfaces greatly increased the adhesion strength between the substrates and stainless-steel coating.The increase in adhesion strength is a result of the formation of favourable mechanical anchoring points.Limitations exist on the surface roughness profile produced by the pulsed waterjet, such that, if the roughness profile generated was copious the coating mirrored the roughened surface profile.Additionally, if the roughness profile produced by the pulsed waterjet was insignificant the coating was removed in its entirety during detachment-based failure.
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 | 0.000 |
| 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".