Development of thermomechanical finite element simulation and experimental investigations on stress fields in laser powder-bed fusion additive manufacturing process
Bibliographic record
Abstract
Additive manufacturing (AM) technology cuts across a large number of industries and applications, and that is part of what makes its potential so compelling. Aerospace, automotive and medical products will drive AM into the future. Laser Powder Bed Fusion (LPBF) is a powder-bed fusion AM process that can be effectively utilized to manufacture structural components with complex geometries. In LPBF, a part is created directly from the three-dimensional model by selectively melting successive powder layers using a laser beam. Nevertheless, there are still some technical barriers and challenges for the production of metallic parts. Optimal production of metallic parts using LPBF requires a comprehensive understanding of the effect of main processing parameters such as laser energy input, powder bed properties and builds conditions. One of the main issues is the identification of ideal process parameters to build a component with minimal induced residual and thermal stresses which are the main cause of distortion. Development of a numerical model to accurately predict the induced residual stresses and distortion during the LPBF process would be of great interest as it would allow to effectively investigate the influence of processing parameters on the quality of the parts. Additionally, a reliable numerical model can drastically reduce the expensive experimental costs associated with the number of tests, cut-ups, as well as manufacturing iterations required for the development of additive manufactured parts. In this study, a high fidelity finite element (FE) model has been developed to numerically simulate the LPBF process in order to predict the induced residual stresses and distortions. A novel multiscale modelling approach has been developed for three dimensional (3D) layer-by-layer simulation of LPBF. First, a microscale FE model has been introduced to predict the melt pool size and temperature profiles. Subsequently, a 3D thermo-mechanical macroscale model has been developed to determine the induced residual stresses and distortions. An extensive experimental investigation has also been conducted to support and validate the developed FE models.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".