Density and mechanical properties in selective laser melting of Invar 36 and stainless steel 316L
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, E T , and critical laser energy density, E C . 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/mm 3 for Invar 36 and 62.5–104.2 J/mm 3 for stainless steel 316L.
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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".