Density and mechanical properties in selective laser melting of Invar 36 and stainless steel 316L
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Bibliographic record
Abstract
In this study, the process-structure-property relationship for selective laser melting of Invar 36 and stainless steel 316L is discussed. Invar 36 and stainless steel 316L have been used in various industrial applications for their unique properties, especially in the aerospace industry. Invar 36 offers a very low coefficient of thermal expansion while stainless steel 316L offers high corrosion resistance. Since both materials are weldable, but hard to machine, this study is aimed at finding the optimum laser process parameters for producing dense components from both alloys. A full factorial design of experiments was formulated in this paper to study a wide range of process parameters for both materials. The bulk density, tensile mechanical properties, fractography, material composition, and residual stresses of the parts produced were investigated. An optimum process window has been suggested based on experimental work. The induced residual stresses were categorized into two categories: microscopic residual stresses and macroscopic residual stresses. The microscopic residual stresses were measured using X-ray diffraction method and the macroscopic residual stresses were measured using cantilever deflection method and finite element simulations. The paper proposes 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. Stable melting ranges were found to occur between these two laser energy densities: 52.1–86.8 J/mm3 for Invar 36 and 62.5–104.2 J/mm3 for stainless steel 316L.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it