A review on additive/subtractive hybrid manufacturing of directed energy deposition (DED) process
Bibliographic record
Abstract
Additive manufacturing (AM) processes are reliable techniques to build highly complex metallic parts. Direct energy deposition (DED) is one of the most common technologies to 3D print metal alloys. Despite a wide range of literature that has discussed the ability of DED in metal printing, weak binding, poor accuracy, and rough surface still exist in final products. Thus, limitations in 3D printing of metal powder and wire indicate post-processing techniques required to achieve high quality in both mechanical properties and surface quality. Therefore, hybrid manufacturing (HM), specifically additive/subtractive hybrid manufacturing (ASHM) of DED has been proposed to enhance product quality. ASHM is a capable process that combines two technologies with 3-axis or multi-axis machines. Different methods have been suggested to increase the accuracy of machines to find better quality and microstructure. In contrast, drawbacks in ASHM still exist such as limitations in existing reliable materials and poor accuracy in machine coordination to avoid collision in the multi-axes machine. It should be noted that there is no review work with focuses on both DED and hybridization of DED processes. Thus, in this review work, a unique study of DED in comparison to ASHM as well as novel techniques are discussed with the objective of showing the capabilities of each process and the benefits of using them for different applications. Finally, new gaps are discussed in ASHM to enhance the layer bonding and surface quality with the processes' effects on microstructures and performance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".