Multi-objective optimization of surface roughness and MRR in AISI 316L stainless steel processed by MQL end milling using taguchi, RSM, ANN, and RFR methods
Bibliographic record
Abstract
This research improves the cutting parameters for end milling AISI 316L stainless steel, a material that is utilized in a variety of sectors, including nuclear power, food, medicine, chemicals, and the marine sector. It has remarkable corrosion resistance. Its great mechanical qualities and limited heat conduction make it challenging to manufacture. When milling with neem oil under Minimum Quantity Lubrication (MQL), the Taguchi technique was utilized to choose the cutting parameters, with an emphasis on Surface Roughness (Ra) and Material Removal Rate (MRR). Important factors such as feed rates, cutting speeds, and cut depths were examined, as well as morphological changes and chip formation. Tool dynamometers were used to quantify MRR, and a surface finish tester was used to evaluate surface roughness. The cutting parameters were optimized and validated using advanced optimization techniques such as Random Forest Regression (RFR), Back Propagation Artificial Neural Network (BPANN), Feed Forward Artificial Neural Network (FFANN), Desirability Function Analysis (DFA), Taguchi Design of Experiments (TDOE), and Response Surface Methodology (RSM). The findings show that machining efficiency is greatly impacted by Material Removal Rate (MRR). While MQL utilizes a prepared Neem oil enhanced tool life and surface quality, higher cutting speeds, feed velocities, and depths of cut increased MRR. At 150 m/min cutting speed, 250 mm/min feed velocity, and 2 mm depth of cut, the best MRR was obtained. At moderate feed velocities, shallow cuts, and medium cutting speeds (100 m/min), surface roughness was reduced. MRR and surface roughness were successfully predicted by the RSM, BPANN, FFANN, and RFR models; RFR proved to be the most accurate.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".