Prediction of residual stresses in metal LPBF parts through a holistic multiscale simulation approach
Bibliographic record
Abstract
Laser Powder Bed Fusion (LPBF) has emerged as a revolutionary additive manufacturing process for the manufacture of complex metallic components, but various challenges still curtail its widespread use by industries. Among these challenges, deformations caused by residual stresses accumulated during printing can lead to severe defects ranging from low geometric accuracy of the as-printed parts to build failure caused by cracking or warpage. Numerical simulation of the LPBF process can help understanding the thermomechanical behavior of a part during printing and predicting its warpage without actually running any costly printing job. In this context, we present a holistic multiscale finite element analysis (FEA) simulation methodology that is able to predict the residual stresses and their associated deformations in metal LPBF parts. A common obstacle in the numerical simulation of the LPBF process is the number of different spatial and temporal scales involved. While part-scale builds usually have a size in the scale of centimeters and take hours to be built, the laser beam operates on a much smaller scale, typically in the scale of micrometers and the timescale of the order of microseconds. Simulating the entire process at the meso-scale level is not tractable in practice and a multiscale simulation methodology is thus warranted. The continuum approach we have developed involves solving two distinct numerical problems at the meso-scale and at the partscale levels, respectively. The meso-scale model is a high-fidelity coupled thermomechanical simulation of the LPBF process that takes into account the dynamics of powder melting and solidification caused by a moving heat source representing the laser beam and its resulting strains and stresses in the material. This problem is solved on a small domain representative of the laser hatch pattern over short time periods (millimeters / seconds) and yields so-called “inherent strains” that are extracted in accordance with the modified Inherent Strain method. The part-scale model uses a layer-by-layer approach, with possible layer lumping, to simulate the LPBF process on full-scale parts. Its simplified physical models give access to the evolution of temperature in the part over the duration of the build and leverages the inherent strains extracted from the meso-scale model to predict the deformations of as-built parts. Our approach will be demonstrated on cantilevers and results will be compared with experimental data.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".