Optimization of Micro-Electrical Discharge Machining Parameters of Ti6AL4V Component: A Mapping Study
Bibliographic record
Abstract
The micro-Electrical Discharge Machining (micro-EDM) is a non-conventional machining process which utilizes electro-thermal, non-contact effects to remove material from the workpiece. Micro-EDM is controlled by many machining parameters and its accuracy is evaluated by performance measures. It is employed when high accuracy and precision are required, especially when difficult-to-machine materials, like titanium alloy Ti6Al4V, are involved. Given the tremendous applications of Ti6Al4V in biomedical devices, automotive, aerospace and microelectromechanical systems, it is valuable to examine thoroughly the micro-EDM of Ti6Al4V component. This work reports a systematic mapping study of 36 papers published in journals and proceedings of conferences in the nearly two decades 2000-2018. First, we divide the papers into categories according to the various optimization techniques applied for the enhancement of micro-EDM machining process of Ti6Al4V component. Then, we discuss the techniques most used and give insight into the current research trends in micro-EDM. Accompanying comments about the use of the mentioned studies for teaching purposes may be of considerable interest for educators.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".