Study of roller burnishing process on En-8 specimens using design of experiments
Bibliographic record
Abstract
Roller burnishing process is a superior cold forming finishing process. It is done on machine or ground surfaces for both external and internal surfaces. In this process, a smooth, hard object (under considerable pressure) rubs over the minute surface irregularities that are produced during machining or shearing. The hardened rolls of the tool press against the surface and deform the protrusions to a more nearly flat geometry. Since the surfaces are cold worked and in residual compression, they possess improved wear and fatigue resistance. The burnishing process is an attractive finishing technique which can increase the work-piece surface finish as well as micro-hardness in a single process, with reduction in tool set-up time which is difficult in conventional processes. The increase in the surface strength mainly serves to increase fatigue behaviour of work-piece under dynamic load. In this study surface roughness and micro-hardness are the main response variables and the process parameters under consideration are spindle speed, tool-feed, number of passes and lubricants. The material under consideration is En-8, which is commonly used industrial standard. Applying Taguchi’s design of experiments on the specimens, the aim is to find optimized values for enhancing the surface quality and hardness economically. The standard orthogonal array L-9 has been used. On experimental analysis, it is found that all the process parameters significantly affect the quality and in EN-8 the micro-hardness values are larger due to work-hardening effect. After the burnishing process no change in surface micro-structure was seen. Key words: Roller burnishing, surface finish, micro-hardness, Taguchi techniques, micro-structure, optimization.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".