Characterisation of machinability of sintered steels during drilling operations
Bibliographic record
Abstract
This study deals with the quantitative evaluation of the machinability of sintered steels during drilling operations. A characterisation technique using scanning electron microscopy and image analysis was developed to characterise quantitatively the amount of flank wear on drillbits. It was shown experimentally, using a drilling test bench, that the evolution of flank wear was proportional to the rate of variation of the thrust force as measured during drilling. Thus, the results show that the slope of the linear region measured on the curve of the thrust force v. the amount of material removed is a more accurate criterion to characterise the machinability of PM products than the average thrust force, which is often suggested in the literature.Furthermore, the effect of the technique used to add MnS to PM powders was investigated. Quantitative characterisation of machinability during drilling operations showed that parts made with steel powders of the type FC–0208 + 0·5 wt-%MnS machine better when the manganese sulphide particles are pre-alloyed rather than admixed. Finally, machinability of parts made with two sinter hardening powders was characterised including a pre-alloyed MnS powder. The results showed that the ‘drillability’ of this type of part is improved when they are in the presintered state rather than when they are in the green state, i.e. unsintered. Moreover, parts made with the sinter hardening powder pre-alloyed with manganese sulphide particles (MnS) showed superior machinability characteristics.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".