Addressing Lubrication Challenges in Extreme Conditions Experienced in Subtractive Processes in Additive-Subtractive Hybrid Manufacturing (ASHM) of AISI H13
Bibliographic record
Abstract
In additive subtractive hybrid manufacturing (ASHM), the combination of machining and additive processes in a single operation combines the benefits of both processes and thus enables the creation of high-quality complex parts. However, this method presents various challenges, particularly during the subtractive or machining steps. The machinability of parts produced through additive manufacturing is often low due to different factors such as high residual stresses and the presence of fine, hard microstructures. Furthermore, in ASHM intermediate heat treatments is not possible which results in subsequent increase in hardness of the printed workpiece material. Additionally, cutting fluids are unsuitable for controlling temperature and friction in ASHM because printing and machining occur simultaneously in the machine. The cutting fluid can contaminate the build environment, affecting layer adhesion and thus being detrimental to the printing operation. This study investigates the use of novel soft metallic lubricant coatings in ASHM, as a substitute for conventional fluid lubricants to address the lubrication challenges in the machining step. The novel proposed coating primarily serves as a lubricating layer between the tool and the workpiece material and provides an effective alternative to cutting fluids. The research aims to assess the effectiveness of these lubricant coatings in improving the surface integrity of additively manufactured parts, exploring a new way of machining without traditional fluid lubricants which is vital for the advancement of ASHM.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".