Laser wire deposition additive manufacturing of Ti-6Al-4V for the aerospace industry
Bibliographic record
Abstract
Additive manufacturing (AM) of alloys has been identified by the aerospace industry as an attractive solution to reduce processing cost of complex components.The interest of the aerospace industry into Ti-6Al-4V resides in its excellent strength to weight ratio and corrosion resistance properties.As of today, control of the process to structure to property relationship for AM technologies remains challenging.This research investigates the effects of some key laser wire deposition (LWD) parameters and various post deposition heat treatments on the developed microstructure and subsequent properties of the deposited Ti-6Al-4V material.First, the effect of two different travel speeds on the structural development of thin Ti-6Al-4V deposits was investigated.A travel speed set at 1.4 mm/s promoted recrystallization of columnar prior β grains into horizontal prior β grains and a diffusion-controlled type of microstructure in the vicinity of the prior β grains boundaries.As a consequence, strength hardly met minimum requirements as set by the AMS4911.A travel speed set at 7.2 mm/s resulted in higher cooling rates that produced a refined microstructure while no recrystallization of the prior β grains has been observed.High strength consistently exceeding minimum wrought requirements were in turn developed.A single stress relief cycle kept the effect of deposition parameters on the developed macrostructures and microstructures post deposition.Strength levels were similar to the ones developed in the as-built condition.The Laser Wire Deposition Additive Manufacturing of Ti-6Al-4V for the Aerospace Industry -McGill University vi AcknowledgementsFirst and foremost, I would like to express my sincere gratitude to my thesis supervisor and friend, Professor Mathieu Brochu for providing me with this one in a lifetime opportunity.This has been a great journey and I sincerely thank you "M" for your support, your guidance and for challenging myself not only on the scientific side but also on the personal side.I sincerely wish that this is not a goodbye, but a new start for
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".