A New Sustainable Approach to Enhancing the Subtractive Process in the Additive–Subtractive Hybrid Manufacturing of AISI H13 Dry Machining
Bibliographic record
Abstract
In additive–subtractive hybrid manufacturing (ASHM), machining and additive processes are combined in a single operation to merge the benefits of both. This method faces challenges, especially during the machining steps. Parts made through additive manufacturing often have low machinability due to factors like residual stresses and fine, hard microstructures. In ASHM, intermediate heat treatments are not possible, leading to the increased hardness of the printed material. Cutting fluids, typically used to reduce temperature and friction, can contaminate the build environment and impair layer adhesion; therefore, they are not recommended in ASHM. This study investigates soft metallic lubricant coatings in ASHM as substitutes for conventional fluid lubricants during dry machining. The coatings form a lubricating layer between the tool and workpiece, providing an alternative to cutting fluids. This research evaluates their effectiveness in improving the surface integrity of additively manufactured parts and supporting dry machining. The results of our research show a 65% reduction in force, a 50% reduction in tool wear, and a reduction in microstructural changes during machining while maintaining dry machining.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".