Influence of thermal properties on residual stresses in SLM of aerospace alloys
Bibliographic record
Abstract
Purpose Residual stresses are induced during selective laser melting (SLM) because of rapid melting, solidification and build plate removal. This paper aims to examine the thermal cycle, residual stresses and part distortions for selected aerospace materials (i.e. Ti-6Al-4V, stainless steel 316L and Invar 36) using a thermo-mechanical finite element model. The numerical results are validated and compared to experimental data. Design/methodology/approach The model predicts the residual stress and part distortion after build plate removal. The residual stress field is validated using X-ray diffraction method and the part distortion is validated using dimensional measurements. Findings The trends found in the numerical results agree with those found experimentally. Invar 36 had the lowest tensile residual stresses because of its lowest coefficient of thermal expansion. The residual stresses of stainless steel 316L were lower than those of Ti-6Al-4V because of its high thermal diffusivity. Research limitations/implications The model predicts residual stresses at the optimal SLM process parameters. However, using any other process conditions could cause void formation and/or alloying element vaporization, which would require the inclusion of melt pool physics in the model. Originality/value The paper explains the influence of the coefficient of thermal expansion and thermal diffusivity on the induced thermal stresses using experimental and numerical results. The methodology can be used to predict the part distortions and residual stresses in complex designs of any of the three materials under optimal SLM process parameters.
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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 | 0.001 |
| 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".