Development of a Thermomechanical Model for Prediction of Residual Stress during Laser Powder-Bed Fusion: Evaluation of Inherent Strain Approach
Bibliographic record
Abstract
The build-up of residual stresses in a part during laser powder-bed fusion (LPBF) provides a significant limitation to the adoption of this process. These residual stresses may cause a part to fail during a build or fall outside the specified tolerances after fabrication. Defectfree production of metallic parts using LPBF requires process optimization, as a crucial step, for effective usage of the process. Development of a numerical model to accurately predict the induced residual stresses and distortion during the LPBF process is of great interest as it allows to effectively investigate the influence of processing parameters on the quality of the parts. In this work, we developed a novel high-fidelity finite element (FE) model based on the inherent strain (local-global) approach to simulate the build process and calculate the residual stress and distortion for Hastelloy X specimens built with a continuous scan strategy. Conclusions from the thermomechanical simulations showed good agreement with X-ray diffraction measurements and 3D scanning data used to determine the residual stresses and distortions in the parts.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".