Flow drilling and flow tapping as a mean to improve the fatigue life of specimens representing plate-type orthopedic implants
Bibliographic record
Abstract
• Flow processing induce ultrafine grain structure and work hardening. • Crest folds are created by flow tapping. • The fatigue life of flow formed holes is shorter than its conventional counterpart. • Folds are preferential fatigue crack initiation sites. • Flow forming increase the amount of sigma phase and delta ferrite. This study investigates the impact of flow drilling and flow tapping on the microstructure, microhardness, and fatigue life of 316L stainless steel specimens, which are designed to simulate plate-type orthopedic implants. In this novel approach, threaded holes are created by combining both cutting and deformation techniques. Three methods are examined: cut drilling with cut tapping, flow drilling with cut tapping, and cut drilling with flow tapping. The resulting specimens are analyzed through microscopic observations, microhardness testing, and four-point bending fatigue tests, conducted in accordance with ASTM standards. The S-N curves and fractographic analyses reveal that cutting methods generally offer superior fatigue performance. At the lowest stress level, the average fatigue life of the flow tapped specimens is 4.10 5 cycles, whereas specimens with holes formed with cutting method exhibit an average fatigue life of 1.5x10 6 . Specifically, cut threads are free of discontinuities, whereas flow threads systematically had crest folds. These folds are sites for crack initiation that contributes to the reduction of fatigue lives. Furthermore, the presence of the sigma phase, related to flow drilling and flow tapping, contribute to material embrittlement, which adversely affects fatigue life. Although deformation methods did not yield encouraging results for the 316L specimens, the characteristics they impart, such as the formation of ultrafine grains, may prove beneficial for other materials and applications. This study is the first to evaluate the fatigue life of threaded holes produced through a combination of cutting and deforming manufacturing methods.
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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 | 0.000 |
| 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".