Experimental studies and optimisation of machining performance measurement for Al7075/n-SiCp composite by using RSM and ANN techniques
Bibliographic record
Abstract
The present study explores how process variables affect tool wear during dry turning of 7075 aluminium alloy composites reinforced with 2% nano-sized silicon carbide (n-SiC). Two predictive modelling methods, Response Surface Methodology (RSM) and Artificial Neural Network (ANN), were used to forecast tool wear in ceramic cutting inserts, both with and without Physical Vapor Deposition (PVD) coating. An organized experimental design was created using the L27 orthogonal array approach. Both RSM and ANN models showed strong predictive ability, with coefficients of determination (R²) over 75% and mean squared error (MSE) below 0.2%. Analysis of variance (ANOVA) indicated that feed rate and cutting speed had the most significant effects on tool wear. Cutting depth, feed rate, and cutting speed were all statistically important factors affecting wear behavior. Abrasive wear was more evident at higher machining settings, while adhesive wear was mostly seen at lower settings. Uncoated tools displayed edge chipping, crater wear, and abrasion, with adhesive wear dominant at low machining parameters and abrasive wear at higher ones. Both RSM and ANN models effectively predicted tool wear during dry turning of n-SiC reinforced Al 7075 composites, with feed rate and cutting speed identified as the most influential factors.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".