Incremental sheet metal forming of Ti–6Al–4V alloy for aerospace application
Bibliographic record
Abstract
Driven by an increasing demand from the aerospace industry, thin sheet forming of titanium and its alloys is gaining prominence in scientific research. The design and manufacture of aerospace components requires the utmost precision and accuracy. It is essential to have good control over the process parameters of the forming process. Processes such as incremental sheet metal forming (ISMF) are highly controlled in the current manufacturing environment, but improvements in geometric accuracy and thinning are still needed. Although ISMF has greater process competence for manufacturing airframe structures with minimal costs, the process has its own negative effect on geometric accuracy due to elastic springback and sheet thinning. In this study, finite element analysis and experimental work are performed, considering process parameters such as spindle speed, feed rate, step depth, and tool diameter, to study the geometric accuracy and thinning of Ti–6Al–4V alloy sheet, while incrementally forming an aerospace component with asymmetrical geometry. The results are useful for understanding the geometric accuracy and thinning effects on parts manufactured by single point incremental forming (SPIF). Results from finite element analysis and experimental work are compared and found to be in good agreement.
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".