Understanding residual stress in functionally graded directed energy deposition
Bibliographic record
Abstract
Residual stress within functionally graded material (FGM) fabricated by directed energy deposition (DED) limits their industrial application. This paper presents a new model for predicting residual stress in the DED-built FGM thin-wall structures, accommodating any DED process configurations or material combinations. To validate this model, SS316/IN718 FGM thin-wall structures were produced by powder-fed laser DED processes, and residual stresses along the longitudinal direction were measured by X-ray diffraction techniques . Under the same deposition parameters , five material composition transition paths from SS316 to IN718 were employed to quantify the effect of material mixing properties on the residual stress distribution . One path was based on the designed weight percentages of SS316 and IN718 in each deposition layer, while the other four paths were determined by detecting the average weight ratios of four elements: iron (Fe), nickel (Ni), niobium (Nb) and titanium (Ti). The predicted residual stress profiles agreed with the measurements, with the maximum normalized root mean squared error (NRMSE) of 26.42 % observed in Nb-based predictions. The validated model was further extended to investigate residual stress distribution in two types of FGM thin-wall structures featured by tilted material transition regions: vertical- and horizontal-dominant walls. Results suggest that maximum tensile residual stresses are proportionally related to the material gradient angle ( θ 1 ) in vertical-dominant walls, while increasing the material gradient angle ( θ 2 ) in horizontal-dominant walls results in more compressive residual stresses at the FGM’s top surface. Based on a comprehensive understanding of the process mechanism using this practical model, it is now possible to propose novel deposition strategies that allow the material specification to be met while offering a reduced residual stress solution.
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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) | 0.000 | 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".