Process-Structure-Property Relationships in Selective Laser Melting of Aerospace Alloys
Bibliographic record
Abstract
Metal additive manufacturing can be used for producing complex and functional components in the aerospace industry. This thesis deals with the process-structure-property relationships in selective laser melting of three aerospace alloys: Invar 36, stainless steel 316L, and Ti-6Al-4V. These alloys are weldable but hard to machine, which make them good candidates for the selective laser melting process. Invar 36 has a very low coefficient of thermal expansion because of its nickel concentration of 36% and stainless steel 316L contains 16-18% chromium that gives the alloy a corrosion resistance property. Ti-6Al-4V offers high strength-to-weight ratio, high biocompatibility, and outstanding corrosion resistance. Any changes in the chemical composition of these materials could affect their performance during application. In this thesis, a full factorial design of experiments is formulated to study a wide range of laser process parameters. The bulk density, tensile mechanical properties, fractography, microstructure, material composition, material phases, coefficient of thermal expansion, magnetic dipole moments, and residual stresses of the parts produced are experimentally investigated. An optimum process window has been suggested for each material based on experimental work. The thermal cycle, residual stresses, and part distortions are examined using a thermo-mechanical finite element model. The model predicts the residual stress and part distortion after build plate removal. The thesis introduces two laser energy densities for each material: brittle-ductile transition energy density, ET, and critical laser energy density, EC. Below the brittle-ductile transition energy density, the parts exhibited void formation, low density, and brittle fracture. Above the critical energy density, the parts showed vaporization of some alloying elements that have low boiling temperatures. Additionally, real-time measurements were taken using a pyrometer and a high-speed camera during the selective laser melting process. The trends found in the numerical results agree with those found experimentally.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".