Design and deposition sequence approaches for enhanced residual stress management in directed energy deposition
Bibliographic record
Abstract
Directed Energy Deposition (DED) Additive Manufacturing (AM) offers significant advantages for fabricating complex geometries and repairing large metal components. However, frequent heating and cooling cycles during the process result in residual stress accumulation, adversely impacting mechanical properties and part reliability. This study investigates strategies to mitigate residual stress through geometric modifications and process optimization, focusing on substrate design, deposition sequences in junctions, and thin-wall structures. An experimentally validated FEM model in SYSWELD was employed to evaluate the effects of incorporating grooves into substrates, optimizing toolpath strategies, and redesigning thin-wall geometries. The results demonstrate that optimized substrate designs, deposition sequences, and geometric configurations for junctions and thin-wall structures effectively reduce residual stress and redirect high-stress regions, enabling enhanced post-processing and improved final part performance. These findings underscore the potential of integrating specialized design and process strategies to improve the reliability and quality of DED-manufactured components.
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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 | 0.001 | 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".