A Comparative Analysis of Chip Shape, Residual Stresses, and Surface Roughness in Minimum-Quantity-Lubrication Turning with Various Flow Rates
Bibliographic record
Abstract
Abstract In this research, an experimental investigation was carried out to investigate the interaction between various turning parameters and different Minimum Quantity Lubrication (MQL) flow rates and compare their effects on chip shape and surface integrity characteristics in low speed turning and high speed turning of AA6061-T6. The turning parameters included cutting speed, feed rate, and depth of cut. The flow rates comprised 3.5, 10, and 15 ml/min, and the surface integrity characteristics consisted of average arithmetic surface roughness, height peak from the valley, axial and hoop surface residual stresses. The results showed that cutting conditions including cutting speed, feed rate, and depth of cut affected chip shape, while MQL flow rate had no impact on chip shape. The lowest values of cutting speed, feed rate, and depth of cut, equal to 145 m/min, 0.07 mm/rev, and 0.66 mm, respectively, resulted in the smallest residual stresses for all the flow rates. Moreover, the smallest surface roughness parameters were obtained at the lowest feed rate (0.07 mm/rev) for all the flow rates, whereas there were high interaction effects between cutting speed and flow rate and depth of cut and flow rate. Finally, turning with the minimum quantity lubrications of 3.5 and 10 ml/min, respectively, are suggested to obtain the best overall surface integrity characteristics. These lower values of flow rate are suitable to reduce machining costs, protect the environment, and preserve machinist’s health. Since most of the previous research studies focused on the comparison of the turning environments including dry, MQL, wet, and cryogenic and only a few research works on the comparative analysis of MQL turning with different flow rates were carried out, the results of the present research can be utilized as a reference for future works in this field.
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 | 0.001 | 0.001 |
| 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 | 0.000 | 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".