Study and Experimental Investigation of the Effect of Progressive Feed Rate on Surface Roughness in CNC End Milling Process Using RSM
Bibliographic record
Abstract
The objective of this research work is to investigate the influence of cutting parameters on the average surface roughness (Ra) in an end milling process. Feed rate (f), Spindle speed (s) and Depth of cut (d) are the cutting parameters considered as significant factors. A case study on the progressive feed (PF) and conventional constant feed (CF) machining characteristics of Aluminum alloy BS L168-T6511 using end milling is considered. Taguchi's design of experiments (DoE) technique is applied for various combinations of cutting factors and average surface roughness was measured using Mitutoyo surftest SJ-301 surface roughness tester. The experimental results of Ra are analyzed by response surface methodology (RSM). The predicted values using the developed regression mathematical model are compared against experimental results and were found in close agreement. ANOVA technique was applied to further analyze the data for checking the model adequacy and to predict the influence of each parameter on output response Ra. Main effect plots, Interaction plots, 3D surface plots, and Contour plots are established. The investigation reveals that output response (Ra) is predominantly affected by feed rate and progressive feed machining (PFM) yields better surface finish than the conventional constant feed machining (CFM) for the end milling.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".