Experimental investigation on friction drilling of titanium alloy
Bibliographic record
Abstract
Friction drilling is a green hole-making process that zealously utilizes the heat generated from the friction between the rotating conical tool and workpiece to create a bushing without generating chip. The difficult-to-machine materials with unique metallurgical properties have been developed to meet the demands of extreme applications. However, the major challenges of friction drilling on difficult-to-machine materials are the hole diameter accuracy, petal formation and tool wear. In this study, the effects of process parameters such as spindle speed and feed rate on bushing height and shape, hardness and tool wear in friction drilling of titanium alloy Ti-6Al-4V were experimentally investigated using tungsten carbide tool. Optical photographs have also been analyzed for better understanding of the chipless friction drilling process for different parametric settings. Experimental results indicated that the spindle speed has great influences for achieving better bushing formation and prolong the tool life. It was confirmed that the low spindle speed and low feed rate have great influences for achieving better bushing shape and height, prolong tool life and lower hardness that located adjacent to the hole wall. It also was discovered that the low thermal conductivity of Ti-6Al-4V caused to improper increment of frictional heat and surface temperature. This disadvantage leads to unsatisfactory bushing formation. This work demonstrated the performances of chipless friction drilling used on difficult-to-machine material that can offer a great prospective for a new product design and manufacturing.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".